{"as_of":"2026-08-18T09:50:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:19c94eb1439959259ce079687467ed8cd54cd66853dd9e543e99a8590f908f0a","coverage":[{"denominator":81,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":81,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T20:57:12.539343Z","state":"measured"},{"denominator":90,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":90,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":9,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":9,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T23:51:05.402554Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-04T17:40:01.069980Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.05268","snapshot_observed_at":"2026-08-06T23:51:05.402554Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.16211","last_updated":"2025-06-19T10:59:53Z","snapshot_observed_at":"2026-08-18T01:55:35.124350Z","submitted_at":"2025-06-19T10:59:53Z","title":"ControlVLA: Few-shot Object-centric Adaptation for Pre-trained Vision-Language-Action Models","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T23:51:05.402554Z"},"links":{"cited_paper":"/paper/2412.05268","citing_paper":"/paper/2506.16211"},"observation_digest":"sha256:2bed003f163d670b2cde1ddc8e6d82b50175cdacf95ca0a4f9b5b397d898e569","observation_id":"31347ab5-89e4-4054-9245-a85a0314e0e4","resolution":{"observed_at":"2026-08-06T23:51:05.402554Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"cited_work":{"arxiv_id":"2412.05268","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.05268","snapshot_observed_at":"2026-07-04T17:40:01.069980Z","title":"Dense- matcher: Learning 3d semantic correspondence for category- level manipulation from a single demo","venue":null,"work_id":"e3d4d376-9f69-4ce1-bcd3-7fd93e4b3280","year":2024},"citing_paper":{"arxiv_id":"2507.00990","last_updated":"2026-05-13T01:35:48Z","snapshot_observed_at":"2026-07-06T21:50:35.488353Z","submitted_at":"2025-07-01T17:39:59Z","title":"Robotic Manipulation by Imitating Generated Videos Without Physical Demonstrations","version":3},"reference_index":138,"source":"pdf_text","source_observed_at":"2026-05-19T06:36:13.144868Z"},"links":{"cited_paper":"/paper/2412.05268","citing_paper":"/paper/2507.00990"},"observation_digest":"sha256:3cf29d86279bd6dffe0eadddcb50881edc65f2294a1624222f07a419e4b44b6c","observation_id":"3e5669af-b5f3-478b-858e-9fcf8a1d8a7a","resolution":{"observed_at":"2026-05-19T06:37:07.333645Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"cited_work":{"arxiv_id":"2412.05268","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.05268","snapshot_observed_at":"2026-07-04T17:40:01.069980Z","title":"Dense- matcher: Learning 3d semantic correspondence for category- level manipulation from a single demo","venue":null,"work_id":"e3d4d376-9f69-4ce1-bcd3-7fd93e4b3280","year":2024},"citing_paper":{"arxiv_id":"2511.02830","last_updated":"2026-04-18T20:04:38Z","snapshot_observed_at":"2026-08-11T13:37:22.395822Z","submitted_at":"2025-11-04T18:58:03Z","title":"Densemarks: Learning Canonical Embeddings for Human Heads Images via Point Tracks","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-18T00:53:24.323059Z"},"links":{"cited_paper":"/paper/2412.05268","citing_paper":"/paper/2511.02830"},"observation_digest":"sha256:4c85dc0622abe25a3aeb658c97693416c7ee4c13a3a2e700004a00f880a62b52","observation_id":"6cd68ccc-db0e-407b-82f7-26af3da40de1","resolution":{"observed_at":"2026-05-18T00:55:35.231966Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"cited_work":{"arxiv_id":"2412.05268","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.05268","snapshot_observed_at":"2026-07-04T17:40:01.069980Z","title":"Dense- matcher: Learning 3d semantic correspondence for category- level manipulation from a single demo","venue":null,"work_id":"e3d4d376-9f69-4ce1-bcd3-7fd93e4b3280","year":2024},"citing_paper":{"arxiv_id":"2512.01773","last_updated":"2026-04-15T17:13:09Z","snapshot_observed_at":"2026-08-03T04:29:49.820139Z","submitted_at":"2025-12-01T15:15:04Z","title":"IGen: Scalable Data Generation for Robot Learning from Open-World Images","version":2},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-05-17T02:58:36.214948Z"},"links":{"cited_paper":"/paper/2412.05268","citing_paper":"/paper/2512.01773"},"observation_digest":"sha256:fc13721c52c4850b00d56256726d2f9160caf2fc80f17a26c4ff9d1fee0f7e56","observation_id":"7cb25555-bcec-4973-b0cf-5b91531cb6c7","resolution":{"observed_at":"2026-05-17T02:58:54.791384Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"cited_work":{"arxiv_id":"2412.05268","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.05268","snapshot_observed_at":"2026-07-04T17:40:01.069980Z","title":"Dense- matcher: Learning 3d semantic correspondence for category- level manipulation from a single demo","venue":null,"work_id":"e3d4d376-9f69-4ce1-bcd3-7fd93e4b3280","year":2024},"citing_paper":{"arxiv_id":"2604.10579","last_updated":"2026-05-30T10:07:21Z","snapshot_observed_at":"2026-08-11T12:29:11.942165Z","submitted_at":"2026-04-12T10:56:31Z","title":"AffordGen: Generating Diverse Demonstrations for Generalizable Object Manipulation with Afford Correspondence","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-10T16:21:40.381114Z"},"links":{"cited_paper":"/paper/2412.05268","citing_paper":"/paper/2604.10579"},"observation_digest":"sha256:048048aa3839be3fa82e5ce5414dda67df1cca54d9d51ca1a359330bcdd78fe2","observation_id":"2dc9fe79-affb-4e1e-94ae-5529aefebe7a","resolution":{"observed_at":"2026-05-11T09:00:59.161695Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.05268","snapshot_observed_at":"2026-07-12T22:32:10.188246Z","title":"Dense- matcher: Learning 3d semantic correspondence for category- level manipulation from a single demo.arXiv preprint arXiv:2412.05268, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2604.10579","last_updated":"2026-05-30T10:07:21Z","snapshot_observed_at":"2026-08-11T12:29:11.942165Z","submitted_at":"2026-04-12T10:56:31Z","title":"AffordGen: Generating Diverse Demonstrations for Generalizable Object Manipulation with Afford Correspondence","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-07-12T22:32:10.188246Z"},"links":{"cited_paper":"/paper/2412.05268","citing_paper":"/paper/2604.10579"},"observation_digest":"sha256:a54b544302b7169d4aca7f45e5bccb59ddd4c8bf305405f95f1aa6e5ee0eed61","observation_id":"e434bea8-e760-462c-b2aa-6de002330e4a","resolution":{"observed_at":"2026-07-12T22:32:10.188246Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"cited_work":{"arxiv_id":"2412.05268","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.05268","snapshot_observed_at":"2026-07-04T17:40:01.069980Z","title":"Dense- matcher: Learning 3d semantic correspondence for category- level manipulation from a single demo","venue":null,"work_id":"e3d4d376-9f69-4ce1-bcd3-7fd93e4b3280","year":2024},"citing_paper":{"arxiv_id":"2605.18039","last_updated":"2026-05-18T08:31:04Z","snapshot_observed_at":"2026-08-15T01:08:18.336672Z","submitted_at":"2026-05-18T08:31:04Z","title":"SGSoft: Learning Fused Semantic-Geometric Features for 3D Shape Correspondence via Template-Guided Soft Signals","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-05-20T11:48:55.446684Z"},"links":{"cited_paper":"/paper/2412.05268","citing_paper":"/paper/2605.18039"},"observation_digest":"sha256:468f8f8704d7eec175350c832bb2d9da3fcd08529f4eab66bb72ba056add47af","observation_id":"402a4108-1848-474f-bacd-e3e64d1c6b4c","resolution":{"observed_at":"2026-05-20T11:53:15.145489Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"cited_work":{"arxiv_id":"2412.05268","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.05268","snapshot_observed_at":"2026-07-04T17:40:01.069980Z","title":"Dense- matcher: Learning 3d semantic correspondence for category- level manipulation from a single demo","venue":null,"work_id":"e3d4d376-9f69-4ce1-bcd3-7fd93e4b3280","year":2024},"citing_paper":{"arxiv_id":"2606.25241","last_updated":"2026-06-23T23:58:57Z","snapshot_observed_at":"2026-08-12T00:51:26.798624Z","submitted_at":"2026-06-23T23:58:57Z","title":"GRAFT: Graph-Based Affordance Transfer via Part Correspondence","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-25T23:27:15.576980Z"},"links":{"cited_paper":"/paper/2412.05268","citing_paper":"/paper/2606.25241"},"observation_digest":"sha256:e562267ffb1952b187ec9baf001af8b93558f3926e1d6f65aa94415883f305ff","observation_id":"09802c85-84ac-4eec-89ad-d934f6feca77","resolution":{"observed_at":"2026-07-04T17:40:01.071620Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.05268","snapshot_observed_at":"2026-08-01T05:06:27.122910Z","title":"arXiv preprint arXiv:2412.05268 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.27592","last_updated":"2026-07-30T02:22:34Z","snapshot_observed_at":"2026-08-06T11:15:56.479411Z","submitted_at":"2026-07-30T02:22:34Z","title":"MeshFM: 2D Features Are All You Need for 3D Shape Understanding","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-01T05:06:27.122910Z"},"links":{"cited_paper":"/paper/2412.05268","citing_paper":"/paper/2607.27592"},"observation_digest":"sha256:668583520a16907277b03756051659e547e6f269f2737b668ab47e8d96a5eceb","observation_id":"ec17ba3c-0a22-43e8-9e8c-1e1873380023","resolution":{"observed_at":"2026-08-01T05:06:27.122910Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2412.05268/citation-record","integrity":"/paper/2412.05268/integrity","json":"/paper/2412.05268/citation-record.json","paper":"/paper/2412.05268"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:12.244780Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.244780Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:09700eae239177d747fd8d0cefb85653e2352c191f5f4d5df2522fb859ac7bac","observation_id":"c5e45e6c-acd0-4b95-aa75-26954773bbde","resolution":{"observed_at":"2026-08-11T20:57:12.244780Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:12.249628Z","title":"@esa (Ref","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.249628Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:dc776c0ed60e2cab58a2c0f1847ac1ae6d3e052314f242b43aedfbec1bde297f","observation_id":"1faf6555-e515-4e9c-b68b-2d41db11b1a8","resolution":{"observed_at":"2026-08-11T20:57:12.249628Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:12.253294Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.253294Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:c01200bb96341c76b63a5fbfe7587c244ace0c4592455986bdd998f3e18e0aec","observation_id":"249dfaab-0502-486d-a70d-5f869664e266","resolution":{"observed_at":"2026-08-11T20:57:12.253294Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.627347Z","title":null,"venue":null,"work_id":"0845c343-38cb-4f3b-a51e-606bbdac037e","year":null},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.257250Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:577b9e6b38c81bd558d0ec3a034ea93cf359f364c2c8547011cd057f851c875d","observation_id":"03cd4437-d790-4cd1-945c-8e288d2fa7d4","resolution":{"observed_at":"2026-08-11T20:57:13.631135Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2003.11996","last_updated":"2020-03-26T16:00:55Z","snapshot_observed_at":"2026-08-15T19:27:41.233818Z","submitted_at":"2020-03-26T16:00:55Z","title":"Accelerated Analog Neuromorphic Computing","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.11996","snapshot_observed_at":"2026-08-11T20:57:12.261603Z","title":"Alliez, E.C","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.261603Z"},"links":{"cited_paper":"/paper/2003.11996","citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:bb3f099aa69782274bc1691629e43127d1f9c091de85d4bf636a1234a90ea8af","observation_id":"954b52c8-adea-4f44-bf01-e9c96c8874a0","resolution":{"observed_at":"2026-08-11T20:57:12.261603Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.05814","last_updated":"2022-10-15T21:18:49Z","snapshot_observed_at":"2026-08-17T08:19:54.748810Z","submitted_at":"2021-12-10T20:15:03Z","title":"Deep ViT Features as Dense Visual Descriptors","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.05814","snapshot_observed_at":"2026-08-11T20:57:12.265574Z","title":"Deep vit features as dense visual descriptors","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.265574Z"},"links":{"cited_paper":"/paper/2112.05814","citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:75fe00778aac2f28776dfb5c07406556cffb2f1fa56fa4fbf578bbcca4c27f8e","observation_id":"87e8edc2-f6c6-417a-9e77-265297dcc0ef","resolution":{"observed_at":"2026-08-11T20:57:12.265574Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.615612Z","title":"The wave kernel signature: A quantum mechanical approach to shape analysis","venue":null,"work_id":"c81960ac-8fb3-4dd9-8ffa-733648d893e3","year":2011},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.269692Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:f8ec6934b674eb8d54417a9d8ac9e0fb5224924ef5d6200f14a8563b8049435c","observation_id":"aadb55f9-2a1c-4e5d-9e66-c4e61b892cab","resolution":{"observed_at":"2026-08-11T20:57:13.619127Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.604702Z","title":"Affordances from human videos as a versatile representation for robotics","venue":null,"work_id":"339a79d0-8bf3-4850-89d6-9c8987c363ed","year":2023},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.273185Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:2edb3820964f9a1721cdeb990a078c64d9c4b67f95ab87dbdee960c501718017","observation_id":"e0e5ef3b-e4b2-4545-b064-767ec5222441","resolution":{"observed_at":"2026-08-11T20:57:13.608943Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2945.81735","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.053773Z","title":"Bernardini, J","venue":null,"work_id":"2e0b0e60-cb71-434f-a7d2-89fea7731557","year":1999},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.277305Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:6e9a86b5b8f46364c9450479925f308ab4c6f4c73a4c281dbc33e9507d9d4b4c","observation_id":"9d188101-fd07-4b9e-9615-3bb351d5f75f","resolution":{"observed_at":"2026-08-11T20:57:13.060063Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.593785Z","title":"Faust: Dataset and evaluation for 3d mesh registration","venue":null,"work_id":"b13606df-0e11-4d8b-8c62-28b2af0eae3b","year":2014},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.281420Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:4a037c914a475459f1b63e30f7530af4416f1c4e956c44fab2b6404d800f8aa6","observation_id":"e7a4fe11-8fb6-4cec-8bcf-1bb7c9a272ff","resolution":{"observed_at":"2026-08-11T20:57:13.597762Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.583360Z","title":null,"venue":null,"work_id":"7c6aff45-67fb-40ed-a1b8-e30e0767ecb1","year":2014},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.285023Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:8307b2c8d76cd2b8517769f3f1200960655f0e9d3c3e178cd8d73d2153770e52","observation_id":"cce88a7e-5072-4f35-a3cd-61153c28e12c","resolution":{"observed_at":"2026-08-11T20:57:13.586721Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.570493Z","title":"Bronstein, Michael M","venue":null,"work_id":"3c1bffc5-96dc-4e2c-b3ab-247394d6e68b","year":2009},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.288518Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:b7adea53bb24985c9ee5f8cbf5728af4295813d6280e9d9d16671c9f0d276c14","observation_id":"7d197a33-ef73-4c3a-86ce-2b97251cb79d","resolution":{"observed_at":"2026-08-11T20:57:13.575210Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.558886Z","title":"Unsupervised deep multi-shape matching","venue":null,"work_id":"bfb74d7f-0e99-4219-abe1-e3924676b8b1","year":2022},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.291894Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:bb03c7adf6f251d958fa34cc0904c2e8bee74087ef4c810814567305822990f8","observation_id":"1bcc37f6-ff88-476d-9196-816754b231b0","resolution":{"observed_at":"2026-08-11T20:57:13.562562Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.14419","last_updated":"2026-05-04T06:34:17Z","snapshot_observed_at":"2026-08-15T04:07:14.691497Z","submitted_at":"2023-04-27T02:12:47Z","title":"Unsupervised Learning of Robust Spectral Shape Matching","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.14419","snapshot_observed_at":"2026-08-11T20:57:12.295212Z","title":"Unsupervised learning of robust spectral shape matching","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.295212Z"},"links":{"cited_paper":"/paper/2304.14419","citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:1c4723016396570070f2855ba0cafeab5108d78645c9015adc8a7a0da2f0f773","observation_id":"dfe91794-3dfd-4b5e-aed7-5143889d59fb","resolution":{"observed_at":"2026-08-11T20:57:12.295212Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.547961Z","title":"Emerging properties in self-supervised vision transformers","venue":null,"work_id":"c4918108-67e9-463b-80d5-64781e2b6463","year":2021},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.298962Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:42c38aad898f21fb6dea90dee27598cd315a1a5dc667e9d9a27331215db82c39","observation_id":"2ad860cb-bdf1-4e1f-baa2-30c471b3dcbb","resolution":{"observed_at":"2026-08-11T20:57:13.551548Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.537780Z","title":"Zero-shot image feature consensus with deep functional maps","venue":null,"work_id":"a5c8fd93-4df3-4e81-a561-487ac1253e09","year":2024},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.302249Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:db055fda0bbf2178f50222b896d1fb1c4cf22ff6d20e024261d7a3b48c7bdb7a","observation_id":"668ca848-34d5-4064-8824-e15ccab7e92a","resolution":{"observed_at":"2026-08-11T20:57:13.541303Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10329","last_updated":"2024-03-06T00:11:34Z","snapshot_observed_at":"2026-08-15T01:06:47.729130Z","submitted_at":"2024-02-15T21:11:50Z","title":"Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10329","snapshot_observed_at":"2026-08-11T20:57:12.308966Z","title":"Universal manipulation interface: In-the-wild robot teaching without in-the-wild robots","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.308966Z"},"links":{"cited_paper":"/paper/2402.10329","citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:3e6503b1455da39d3a10d7ebc288eb5974f0f484773a163cfa8213d6c6e11a85","observation_id":"cec5502c-76a9-4ef5-accb-f16a20b9316f","resolution":{"observed_at":"2026-08-11T20:57:12.308966Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1404.3785","last_updated":"2014-04-15T00:24:12Z","snapshot_observed_at":"2026-08-14T23:37:31.224612Z","submitted_at":"2014-04-15T00:24:12Z","title":"Reducing the Barrier to Entry of Complex Robotic Software: a MoveIt! Case Study","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1404.3785","snapshot_observed_at":"2026-08-11T20:57:12.313396Z","title":"Reducing the barrier to entry of complex robotic software: a moveit! case study","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.313396Z"},"links":{"cited_paper":"/paper/1404.3785","citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:2fa19c943fe5323ced40a55c0f02d921610c15798f01f96696e42e137975e567","observation_id":"3cfcdfbf-db40-4336-ad46-87a864e85545","resolution":{"observed_at":"2026-08-11T20:57:12.313396Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1204.6216","last_updated":"2012-09-12T16:53:47Z","snapshot_observed_at":"2026-08-17T13:03:10.175993Z","submitted_at":"2012-04-24T20:26:58Z","title":"Geodesics in Heat","version":2},"cited_work":{"arxiv_id":"1204.6216","doi":null,"metadata_source":"pith","pith_arxiv_id":"1204.6216","snapshot_observed_at":"2026-08-11T20:57:12.950202Z","title":"Geodesics in Heat","venue":"cs.GR","work_id":"4f0367df-f597-4513-8ec3-f440421bfd9d","year":2012},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.317551Z"},"links":{"cited_paper":"/paper/1204.6216","citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:afda12b9b1db242a115b96cd19f1f8f6a065cbccc5c0073edcef9491a499764a","observation_id":"5af76bba-2f13-42b7-8e7c-bc7dfc8d67d3","resolution":{"observed_at":"2026-08-11T20:57:12.954495Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.05663","last_updated":"2023-07-11T17:57:40Z","snapshot_observed_at":"2026-08-17T00:31:15.553571Z","submitted_at":"2023-07-11T17:57:40Z","title":"Objaverse-XL: A Universe of 10M+ 3D Objects","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.05663","snapshot_observed_at":"2026-08-11T20:57:12.321260Z","title":"Objaverse-xl: A universe of 10m+ 3d objects, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.321260Z"},"links":{"cited_paper":"/paper/2307.05663","citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:e9144be810e0b304a0af0b8fcbacd662d22c06f8dea36c91ea876d02afdf9e99","observation_id":"a2faa9cd-4cc3-43d6-bb62-aca7ddcbba6d","resolution":{"observed_at":"2026-08-11T20:57:12.321260Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:12.324597Z","title":"Imagenet: A large-scale hierarchical image database","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.324597Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:df0c7bd4317f8e67caa294a08564835405dd486b7451199f89edc8b02fa21aee","observation_id":"1e77e7bd-e71f-43e6-a7fd-e2c8c4a497c9","resolution":{"observed_at":"2026-08-11T20:57:12.324597Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.526559Z","title":"Deep geometric functional maps: Robust feature learning for shape correspondence","venue":null,"work_id":"57862cd2-7972-4ebf-8ed0-702ebce5a834","year":2020},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.328266Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:824fa5f283828b7616c13aad75da0bcc3a4a7260f98f7ed2c19035f500e52c0d","observation_id":"cbe80c7a-f70b-4ad1-8755-c7dddea4bb91","resolution":{"observed_at":"2026-08-11T20:57:13.530414Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.17024","last_updated":"2024-04-02T19:11:35Z","snapshot_observed_at":"2026-08-16T14:39:32.076924Z","submitted_at":"2023-11-28T18:27:15Z","title":"Diffusion 3D Features (Diff3F): Decorating Untextured Shapes with Distilled Semantic Features","version":2},"cited_work":{"arxiv_id":"2311.17024","doi":null,"metadata_source":"pith","pith_arxiv_id":"2311.17024","snapshot_observed_at":"2026-08-11T20:57:12.856780Z","title":"Diffusion 3D Features (Diff3F): Decorating Untextured Shapes with Distilled Semantic Features","venue":"cs.CV","work_id":"580d0bc7-b77e-44cd-b450-87874aba3b6c","year":2023},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.331478Z"},"links":{"cited_paper":"/paper/2311.17024","citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:6c5d12aeb6708a6e1b7acaa9618e3e00b8449546ecbfed989722e7e91eedfa4f","observation_id":"c2211a54-f3af-4061-9bb3-69a6503d7595","resolution":{"observed_at":"2026-08-11T20:57:12.860686Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.515566Z","title":"Dyke, Caleb Stride, Yu-Kun Lai, and Paul L","venue":null,"work_id":"6cd8e42c-d147-46b2-84f3-8cb89aec28f8","year":2019},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.334956Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:f4b0b919ddab58b1e7cd71d33c58a3220251d7fd6db896fa59a5547742c20f05","observation_id":"17ccf17c-0e73-4337-9910-bee137064c3b","resolution":{"observed_at":"2026-08-11T20:57:13.519430Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.504772Z","title":"Anygrasp: Robust and efficient grasp perception in spatial and temporal domains","venue":null,"work_id":"4173e348-5a54-4658-be5c-52e60e077f0d","year":2023},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.338260Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:1480e5b1461588209dfd9eab99cd9bfc14f89f3924f80ff1f240dabc50d1324c","observation_id":"632073d8-7053-460e-8cd6-8fb0a8cdd27a","resolution":{"observed_at":"2026-08-11T20:57:13.508688Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.08756","last_updated":"2018-09-07T17:53:32Z","snapshot_observed_at":"2026-08-14T19:00:26.695950Z","submitted_at":"2018-06-22T16:38:01Z","title":"Dense Object Nets: Learning Dense Visual Object Descriptors By and For Robotic Manipulation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.08756","snapshot_observed_at":"2026-08-11T20:57:12.341567Z","title":"Dense object nets: Learning dense visual object descriptors by and for robotic manipulation","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.341567Z"},"links":{"cited_paper":"/paper/1806.08756","citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:1fc78b3af3589b150397419f37b4a4188a40e793bf54977691dc20cb24ea45f5","observation_id":"06970799-794e-4f9e-bbe8-febfcd84ce1a","resolution":{"observed_at":"2026-08-11T20:57:12.341567Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.493052Z","title":"Brandt, Axel Feldmann, Zhoutong Zhang, and William T","venue":null,"work_id":"ed16b714-e238-4e7a-b3f5-a3d576f0f3ee","year":2024},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.345415Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:645b99baaf4283e424695ccedfcb094c569ce5b91638a87a5d8a6580a5504cd6","observation_id":"c9ee3833-da66-4223-98b4-2e4e65a5d0fb","resolution":{"observed_at":"2026-08-11T20:57:13.496972Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.481676Z","title":"Riemann: Near real-time se (3)-equivariant robot manipulation without point cloud segmentation","venue":null,"work_id":"b99f6f87-9e98-42fe-b2fc-2fb48260ea99","year":2024},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.348586Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:e5c7bb2e0c26747cfe323385e5585defb9e151b6a4d30a00877e67a6f48597e9","observation_id":"ac0a1d67-ba0d-43f0-af3c-858b4403a6f5","resolution":{"observed_at":"2026-08-11T20:57:13.485375Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.470050Z","title":"Can pre-trained text-to-image models generate visual goals for reinforcement learning? NeurIPS, 36, 2024 b","venue":null,"work_id":"326bb095-4752-4e25-82c7-fa51d788ad7b","year":2024},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.351906Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:6af9ba01c8f7ed03987ef8a76e7828dad4cc39b0c939636cac26169c766abee4","observation_id":"dccea46c-a3a5-4ee3-8445-15a85ffb69c5","resolution":{"observed_at":"2026-08-11T20:57:13.474369Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.457621Z","title":"3 d - coded : 3 d c orrespondences by d eep d eformation","venue":null,"work_id":"70921c42-a214-472c-bf7b-bfa4156f784c","year":2018},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.355074Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:912b4af7caaf60bb4d48bba72bb145f9dd339800cb7dc21e5a932383b4e47b8a","observation_id":"f2c66d2f-b616-449c-8457-d90e972b2474","resolution":{"observed_at":"2026-08-11T20:57:13.461230Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.05228","last_updated":"2018-07-27T09:06:24Z","snapshot_observed_at":"2026-08-14T19:04:04.134754Z","submitted_at":"2018-06-13T19:07:37Z","title":"3D-CODED : 3D Correspondences by Deep Deformation","version":2},"cited_work":{"arxiv_id":"1806.05228","doi":null,"metadata_source":"pith","pith_arxiv_id":"1806.05228","snapshot_observed_at":"2026-08-11T20:57:12.829519Z","title":"3D-CODED : 3D Correspondences by Deep Deformation","venue":"cs.CV","work_id":"80414009-a385-4edd-a1a4-519007584553","year":2018},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.358402Z"},"links":{"cited_paper":"/paper/1806.05228","citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:b1e529ed1dca0ee19ea8a60b30cc65b8d696cc6fd5f46ad8580917d2aba1d2ea","observation_id":"09c6905b-1839-4439-905d-2cc98b1e7b82","resolution":{"observed_at":"2026-08-11T20:57:12.833918Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.444684Z","title":"Unsupervised learning of dense shape correspondence","venue":null,"work_id":"319f2f7c-e8cc-4289-9f66-faaa443f16fe","year":2019},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.361862Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:2de824e3d312edb413f71e26bb591e0f82d9754f1e452a462ec0652b0034c7cc","observation_id":"4b741dee-2bb7-4b98-9915-e0c66996f8f7","resolution":{"observed_at":"2026-08-11T20:57:13.448567Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.433299Z","title":"Proposal flow: Semantic correspondences from object proposals","venue":null,"work_id":"554e1053-2e1f-4922-b80d-c1a36f267b7c","year":2017},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.366514Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:4d23fc7a0fdabfa4278a618062cb0798bdf3d54c7ad6a399bccb92be7e21af04","observation_id":"3773a5b1-a345-4a39-a5a1-1200c562ec6c","resolution":{"observed_at":"2026-08-11T20:57:13.437171Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:12.370160Z","title":"Unsupervised semantic correspondence using stable diffusion","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.370160Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:4eeae90ea8f2f84ac6f879cde98fce881759a64dedc2564d47794125b602e898","observation_id":"1e46f39d-f3a2-4da6-81c1-d3636ceecb62","resolution":{"observed_at":"2026-08-11T20:57:12.370160Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.04919","last_updated":"2024-11-13T08:32:27Z","snapshot_observed_at":"2026-08-17T19:36:58.747803Z","submitted_at":"2024-11-07T17:56:16Z","title":"Stem-OB: Generalizable Visual Imitation Learning with Stem-Like Convergent Observation through Diffusion Inversion","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.04919","snapshot_observed_at":"2026-08-11T20:57:12.374272Z","title":"Stem-ob: Generalizable visual imitation learning with stem-like convergent observation through diffusion inversion","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.374272Z"},"links":{"cited_paper":"/paper/2411.04919","citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:d1509f4ddd92f3566877dad0295edb76f1c2f3251f292e99973d7f64ea61917a","observation_id":"c6b28407-924e-4394-86b3-4e76707d7a6c","resolution":{"observed_at":"2026-08-11T20:57:12.374272Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.414259Z","title":"Robo-abc: Affordance generalization beyond categories via semantic correspondence for robot manipulation","venue":null,"work_id":"da438b53-d9d2-41ce-9981-0e3a076beeeb","year":2024},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.378054Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:a50d2291b33753d32a1772e3aeba487cca6166f22505d8b6ae3738ac42c891a9","observation_id":"974fa5ed-70f9-4113-89fe-8e42fbda0083","resolution":{"observed_at":"2026-08-11T20:57:13.418289Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"1957.12819","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:12.799145Z","title":"Kazhdan, Matthew Bolitho, and Hugues Hoppe","venue":null,"work_id":"4eebf2af-4724-41a7-a781-e98ebc35e592","year":2006},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.381378Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:e8bfbaa95d52c38df7935892d27b3d75b1c9be42c80aa293f6224bb8f95a0170","observation_id":"88d4e2a7-9267-4bbb-8b4d-4d58dd7591a4","resolution":{"observed_at":"2026-08-11T20:57:12.807438Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.06615","last_updated":"2025-03-28T22:16:37Z","snapshot_observed_at":"2026-08-16T13:19:42.475041Z","submitted_at":"2024-09-10T16:11:57Z","title":"One-Shot Imitation under Mismatched Execution","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.06615","snapshot_observed_at":"2026-08-11T20:57:12.384806Z","title":"One-shot imitation under mismatched execution","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.384806Z"},"links":{"cited_paper":"/paper/2409.06615","citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:ab510ac97ed37e889f128abbccb09c58fa6c6d75e9996c307d4891969174d0e3","observation_id":"8764528e-c953-4dca-aaf7-595f8ebf8c60","resolution":{"observed_at":"2026-08-11T20:57:12.384806Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.18121","last_updated":"2024-09-26T17:57:16Z","snapshot_observed_at":"2026-08-16T13:15:00.855537Z","submitted_at":"2024-09-26T17:57:16Z","title":"Robot See Robot Do: Imitating Articulated Object Manipulation with Monocular 4D Reconstruction","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.18121","snapshot_observed_at":"2026-08-11T20:57:12.388305Z","title":"Robot see robot do: Imitating articulated object manipulation with monocular 4d reconstruction","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.388305Z"},"links":{"cited_paper":"/paper/2409.18121","citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:6665fd2b3ad6f5c377509f7a9a5c29b1d629604f02b0f8e5bcf6cae1126ac2ef","observation_id":"61662e6d-5442-4d09-8bcc-7d7933abe3fd","resolution":{"observed_at":"2026-08-11T20:57:12.388305Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.402236Z","title":"Blended intrinsic maps","venue":null,"work_id":"58871d81-a7b9-4a3a-b2d0-6cf705fac9dd","year":2011},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.391821Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:e6c966b913a3aa7d5b43de5ef102a332edd504ec440f638d7d8a38624ce76516","observation_id":"23fbd966-128e-4afb-8ba9-f214d236659c","resolution":{"observed_at":"2026-08-11T20:57:13.406530Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-08-17T19:26:44.032537Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-11T20:57:12.395296Z","title":"Kingma and Jimmy Ba","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.395296Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:65d034156318c308d7d7689fd8c3df05e319f93edc3d562464fd27e9fa038ca0","observation_id":"9008c264-66f3-44b4-95a9-07456178b139","resolution":{"observed_at":"2026-08-11T20:57:12.395296Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.04689","last_updated":"2024-07-05T17:50:38Z","snapshot_observed_at":"2026-08-16T13:36:42.466358Z","submitted_at":"2024-07-05T17:50:38Z","title":"RAM: Retrieval-Based Affordance Transfer for Generalizable Zero-Shot Robotic Manipulation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.04689","snapshot_observed_at":"2026-08-11T20:57:12.398840Z","title":"Ram: Retrieval-based affordance transfer for generalizable zero-shot robotic manipulation, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.398840Z"},"links":{"cited_paper":"/paper/2407.04689","citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:924c9b91d1a3b51b566a303499be40d022a2b2815625cf647d3a18a1cd0e69d6","observation_id":"6d3f5097-46fc-4346-9b32-016b75a074af","resolution":{"observed_at":"2026-08-11T20:57:12.398840Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.17521","last_updated":"2024-04-26T16:40:17Z","snapshot_observed_at":"2026-08-16T13:57:29.761262Z","submitted_at":"2024-04-26T16:40:17Z","title":"Ag2Manip: Learning Novel Manipulation Skills with Agent-Agnostic Visual and Action Representations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.17521","snapshot_observed_at":"2026-08-11T20:57:12.402274Z","title":"Ag2manip: Learning novel manipulation skills with agent-agnostic visual and action representations","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.402274Z"},"links":{"cited_paper":"/paper/2404.17521","citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:441e2014be7ec636a4a991a6530434de8d383bad934b729abc1c99bedb5a3827","observation_id":"953520f3-f579-4eb5-adc8-0cba37bbf9e3","resolution":{"observed_at":"2026-08-11T20:57:12.402274Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.390860Z","title":"Dynamic 3d gaussians: Tracking by persistent dynamic view synthesis","venue":null,"work_id":"6725133d-2228-44fb-9b36-096d42aa115e","year":2024},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.405970Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:4f1c87f841476292400853fc35ee216cd324f26b55b8ff199962f68f61ee6bab","observation_id":"79161979-606f-4536-be6e-aedc5cd44a05","resolution":{"observed_at":"2026-08-11T20:57:13.394599Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.379633Z","title":"Diffusion hyperfeatures: Searching through time and space for semantic correspondence","venue":null,"work_id":"b1ba3896-bebc-438f-9067-e7cdbdd6d03b","year":2023},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.409502Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:112df7ddce70b0e4091659b326f09e4a9cb86e769e3698eb3ce3e2f7e1121ad3","observation_id":"ba176310-8f57-443d-b694-a6076c0d4400","resolution":{"observed_at":"2026-08-11T20:57:13.383483Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.367315Z","title":"Diffusion hyperfeatures: Searching through time and space for semantic correspondence","venue":null,"work_id":"ed1281d2-cd88-428b-9d66-40bf0edf13a5","year":2024},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.412869Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:6599e79223332a055e701ae863354458db8a9402ecf1cff71a0d0824f4912962","observation_id":"06f32164-a160-4509-922a-8cf9688250b4","resolution":{"observed_at":"2026-08-11T20:57:13.370834Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.355591Z","title":"Discrete differential-geometry operators for triangulated 2-manifolds","venue":null,"work_id":"13985772-45b0-4ad9-a2a3-184cf98aaed9","year":2003},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.416015Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:51167c811e8bea6d6a30d47278a31d572bb94844d1b6a128ca9ef496c3f3d2d8","observation_id":"93222aaa-4120-443d-bd06-9d65f0dae4f3","resolution":{"observed_at":"2026-08-11T20:57:13.359067Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:12.419353Z","title":"Nerf: Representing scenes as neural radiance fields for view synthesis","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.419353Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:2d3f9fb0066d56229816317936658060a499198c735a7dfd02529be8e37720a1","observation_id":"d765abcf-1227-46b5-9775-732f475ad943","resolution":{"observed_at":"2026-08-11T20:57:12.419353Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.337619Z","title":"PyMeshLab , January 2021","venue":null,"work_id":"5d6d37ee-b5a4-427c-b6cf-ce7341045905","year":2021},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.422708Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:9590b5b9971068765dff9d4132fe49eb8cc8c025132198603e77928252839ba7","observation_id":"bc1fd11c-83b4-42ed-8d3d-8db08fd4b2fe","resolution":{"observed_at":"2026-08-11T20:57:13.341232Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.326594Z","title":"Informative descriptor preservation via commutativity for shape matching","venue":null,"work_id":"ec15e2a5-6ea5-47b5-84c6-4d37d832dcee","year":2017},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.426352Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:819bc87fb765c5e08836550f75bc2ab9f9e8e639df3218d01679cc3c82e505a7","observation_id":"c475631c-971a-4f81-b56f-bc1a68b9d416","resolution":{"observed_at":"2026-08-11T20:57:13.330481Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.315417Z","title":"Neural congealing: Aligning images to a joint semantic atlas","venue":null,"work_id":"827df1c0-7bb6-4c04-9407-eca18aa0bc87","year":2023},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.429655Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:f923cd6f6be052800664b1dedca86c5e56c3a0f7ecbbbdd8b9d8ac27a2cf7c09","observation_id":"5d7b3a6c-3657-47ca-a810-2d5827150911","resolution":{"observed_at":"2026-08-11T20:57:13.319213Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.07193","last_updated":"2024-02-02T10:24:09Z","snapshot_observed_at":"2026-08-17T13:03:40.359628Z","submitted_at":"2023-04-14T15:12:19Z","title":"DINOv2: Learning Robust Visual Features without Supervision","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.07193","snapshot_observed_at":"2026-08-11T20:57:12.436633Z","title":"Dinov2: Learning robust visual features without supervision, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.436633Z"},"links":{"cited_paper":"/paper/2304.07193","citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:db984add0024158cedb95009764272db4d06343131dbdcd7b5b73217a345f82d","observation_id":"d1395cad-ea54-414d-b5fa-3123aff4178b","resolution":{"observed_at":"2026-08-11T20:57:12.436633Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.304739Z","title":"Functional maps: a flexible representation of maps between shapes","venue":null,"work_id":"aec83957-9fd2-4365-be53-e1e273cb8631","year":2012},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.440473Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:e6a1154317dda6ab0997a938e8059c81376fa4c55e9f6435034a6693cebdaf9b","observation_id":"521cc3e6-6c66-404f-be5a-466f9544fb67","resolution":{"observed_at":"2026-08-11T20:57:13.308242Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.293064Z","title":"Learning so (3)-invariant semantic correspondence via local shape transform","venue":null,"work_id":"a169882a-8879-4940-86d2-0664112a618e","year":2024},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.443690Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:43dd4ba45abb2fe603af10199cbdd79f9ad4e4e0b16b59f4a39e1118e149118f","observation_id":"20050c3d-e6b2-4bba-ac2a-b06218aee886","resolution":{"observed_at":"2026-08-11T20:57:13.297089Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1912.01703","last_updated":"2019-12-03T22:06:05Z","snapshot_observed_at":"2026-07-06T08:41:49.632205Z","submitted_at":"2019-12-03T22:06:05Z","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.01703","snapshot_observed_at":"2026-08-11T20:57:12.447833Z","title":"Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala","venue":null,"work_id":null,"year":1912},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.447833Z"},"links":{"cited_paper":"/paper/1912.01703","citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:806106064813a6f607a4718ea37da7f3f8f074d4f98f86dfc0c0d8922ce62734","observation_id":"8bb159a7-8374-4157-8f94-1c16a7910aef","resolution":{"observed_at":"2026-08-11T20:57:12.447833Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.282249Z","title":null,"venue":null,"work_id":"b8fa0fce-99e6-43d8-ad53-42ec3d3c07d5","year":2016},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.451802Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:75865ef3ab488047dec66add38f10830511f7bd75727480d7ff660aa84ff5bf7","observation_id":"e035bd18-8190-443b-b261-bb56335d2a68","resolution":{"observed_at":"2026-08-11T20:57:13.285759Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1612.00593","last_updated":"2017-04-10T22:25:25Z","snapshot_observed_at":"2026-08-14T21:27:28.037090Z","submitted_at":"2016-12-02T08:40:40Z","title":"PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1612.00593","snapshot_observed_at":"2026-08-11T20:57:12.455717Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.455717Z"},"links":{"cited_paper":"/paper/1612.00593","citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:5c828916ca9264c28b6e10d11a00275b08243f3423d21bdb0bbb2a46cd13feea","observation_id":"b69bccc7-7e48-4829-8d1d-be7c69561bd1","resolution":{"observed_at":"2026-08-11T20:57:12.455717Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.270871Z","title":"Continuous and orientation-preserving correspondences via functional maps","venue":null,"work_id":"df5c6d32-20a1-4c49-b48f-0f95b2141bfc","year":2018},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.459157Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:01417e4777afbd57ac444f161cccadf1b04bde5ef765d1f03fa7112254614e21","observation_id":"9f4e1f4c-59a6-49e1-ab8a-c15c8ff330d9","resolution":{"observed_at":"2026-08-11T20:57:13.274870Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.258853Z","title":"Partial functional correspondence","venue":null,"work_id":"2e53a39a-703f-4be6-a1e6-01256bf11949","year":2017},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.462107Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:d4ab4d94bc8e5f960eea1aed26ab1edd3bc78c762fd0e9ec2d8a4bf04b2dc2da","observation_id":"e4050dc2-3960-4d6b-b45d-0fa590f7ab69","resolution":{"observed_at":"2026-08-11T20:57:13.262851Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.248540Z","title":"Spidermatch: 3d shape matching with global optimality and geometric consistency","venue":null,"work_id":"4af8c661-7711-440e-bb0f-e220a58fed94","year":2024},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.465525Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:e2cbf36764c7efa017e2149b3a1a2d3057eae9c634b2ef3ebd448bed45351114","observation_id":"970687d7-7d83-4f70-8b7f-1f0573f3d17e","resolution":{"observed_at":"2026-08-11T20:57:13.251999Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2112.10752","last_updated":"2022-04-13T11:38:44Z","snapshot_observed_at":"2026-07-06T12:20:47.369918Z","submitted_at":"2021-12-20T18:55:25Z","title":"High-Resolution Image Synthesis with Latent Diffusion Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.10752","snapshot_observed_at":"2026-08-11T20:57:12.468991Z","title":"High-resolution image synthesis with latent diffusion models","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.468991Z"},"links":{"cited_paper":"/paper/2112.10752","citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:3d4862f5e2ac9a9b9068b822acf76ff73f1f4ecde83f85c63b9113d9f3641908","observation_id":"b5282e2c-3989-43fb-9f6e-4eb751e32e89","resolution":{"observed_at":"2026-08-11T20:57:12.468991Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.237701Z","title":"Understanding human hands in contact at internet scale","venue":null,"work_id":"3e2bdb2f-5a7e-46f9-ab77-c215fcdb1842","year":2020},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.472725Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:362f0d34be83b8eb79a98478ec854f1a384a3612b2135ad2e2c7a746be4812a7","observation_id":"efa078f4-6621-4a5e-b119-6398b1b747ff","resolution":{"observed_at":"2026-08-11T20:57:13.241148Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.226099Z","title":"Diffusionnet: Discretization agnostic learning on surfaces","venue":null,"work_id":"f0ebafeb-7528-42de-9df8-b0a36039fd67","year":2022},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.475906Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:9784678ca1c7455a771599205be6fb41ff178c22a44b3ba26acbc8152569037c","observation_id":"41159c3e-11e2-45c1-8423-b737a3b33e02","resolution":{"observed_at":"2026-08-11T20:57:13.229978Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.214132Z","title":"A concise and provably informative multi-scale signature based on heat diffusion","venue":null,"work_id":"b4e3758e-ed97-4191-a360-9ad5c02f8636","year":2009},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.479370Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:182439f77d1fd5afd7d032a99c46c0d6836b97ef7f13d8e7985eb427868a2543","observation_id":"6ee47ca2-eaaa-4590-956d-d7d32989d29f","resolution":{"observed_at":"2026-08-11T20:57:13.218100Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.202843Z","title":"Emergent correspondence from image diffusion","venue":null,"work_id":"38fcd958-d639-4e9b-b9b0-07201b49003d","year":2023},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.482753Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:14799919e8e6480357c6398bfed0e0e5c816e8fe7b163bbaba78ac8496435c09","observation_id":"31be6a8b-7162-4206-adca-c5bcc8d04a7c","resolution":{"observed_at":"2026-08-11T20:57:13.206942Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.191590Z","title":"Robotap: Tracking arbitrary points for few-shot visual imitation","venue":null,"work_id":"e14183fc-50bd-4755-8882-ffe55027ba55","year":2024},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.485904Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:7b289f3dd108fa59dbfa4510a74ec2f43a58f07cdd1a97386ff06a6eb498aa11","observation_id":"0174d1fc-ffd0-4879-9fc1-59b28e758cdc","resolution":{"observed_at":"2026-08-11T20:57:13.195244Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.12077","last_updated":"2023-10-18T16:13:35Z","snapshot_observed_at":"2026-08-16T14:51:00.928885Z","submitted_at":"2023-10-18T16:13:35Z","title":"One-Shot Imitation Learning: A Pose Estimation Perspective","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.12077","snapshot_observed_at":"2026-08-11T20:57:12.488991Z","title":"One-shot imitation learning: A pose estimation perspective","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.488991Z"},"links":{"cited_paper":"/paper/2310.12077","citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:259f93a6c1dde24965f6166ab2d86f611aeb74a8cc6741707fd72feb794b7705","observation_id":"4e52559b-cf64-4124-9d76-c078931954ad","resolution":{"observed_at":"2026-08-11T20:57:12.488991Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.12422","last_updated":"2023-10-13T05:44:37Z","snapshot_observed_at":"2026-08-16T15:53:03.955730Z","submitted_at":"2023-02-24T02:54:15Z","title":"MimicPlay: Long-Horizon Imitation Learning by Watching Human Play","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.12422","snapshot_observed_at":"2026-08-11T20:57:12.492582Z","title":"Mimicplay: Long-horizon imitation learning by watching human play","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":69,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.492582Z"},"links":{"cited_paper":"/paper/2302.12422","citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:2c8d05e47f394c48faa18b169e6aa91c23e2aa737956e8186e600f966bfd0205","observation_id":"f7a57ba1-1492-4bc2-a2e1-c71172ec0171","resolution":{"observed_at":"2026-08-11T20:57:12.492582Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.07788","last_updated":"2024-07-04T04:35:04Z","snapshot_observed_at":"2026-08-17T22:54:39.234703Z","submitted_at":"2024-03-12T16:23:49Z","title":"DexCap: Scalable and Portable Mocap Data Collection System for Dexterous Manipulation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.07788","snapshot_observed_at":"2026-08-11T20:57:12.495938Z","title":"Dexcap: Scalable and portable mocap data collection system for dexterous manipulation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":70,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.495938Z"},"links":{"cited_paper":"/paper/2403.07788","citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:309e4adbf20f671be7c1ff266b3041eb2b6e0826b2516bd0036ea14cca8894ab","observation_id":"b47a1bb6-8715-45ee-9fe4-0bdf07c0b9fc","resolution":{"observed_at":"2026-08-11T20:57:12.495938Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.179865Z","title":"Learning and Reasoning with Visual Correspondence in Time","venue":null,"work_id":"dc6e8032-6891-4eda-a5ef-58d2fe521a58","year":2019},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":71,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.499107Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:7dbd116c7421aff404a2b51cfe0dbdbcda320eea7f1fce0fab3fca019b9c2911","observation_id":"7df45b97-668f-4fdc-a823-0444b7da120e","resolution":{"observed_at":"2026-08-11T20:57:13.183772Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2309.16118","last_updated":"2024-10-16T20:30:58Z","snapshot_observed_at":"2026-08-16T14:57:00.523715Z","submitted_at":"2023-09-28T02:50:16Z","title":"D$^3$Fields: Dynamic 3D Descriptor Fields for Zero-Shot Generalizable Rearrangement","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.16118","snapshot_observed_at":"2026-08-11T20:57:12.502649Z","title":"D ^3 fields: Dynamic 3d descriptor fields for zero-shot generalizable robotic manipulation, 2023 b","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":72,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.502649Z"},"links":{"cited_paper":"/paper/2309.16118","citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:6982f9b6cb34aa3936ac42985d03266afbc2f1b5799b5598a0d11461554dfdbf","observation_id":"bb9b2710-785a-474f-99a7-7eae4ff113c3","resolution":{"observed_at":"2026-08-11T20:57:12.502649Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.167479Z","title":"Dynamic graph cnn for learning on point clouds","venue":null,"work_id":"6973b772-efbc-4f67-9381-258c95195d49","year":2019},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":73,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.507248Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:7310565f941158e4a41e3728b36f4c15f085bc41a67fff3e4580e78af15937e0","observation_id":"8865cd4c-8974-4a0d-8895-7e4b4ca1c4c4","resolution":{"observed_at":"2026-08-11T20:57:13.171271Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.156095Z","title":"Omniobject3d: Large-vocabulary 3d object dataset for realistic perception, reconstruction and generation","venue":null,"work_id":"08fb04f2-cb21-4026-a721-cd464d04c859","year":2023},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":74,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.510352Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:4f5e752ea19f148a52bcdc2300ea4e2894b28a7c278256476589a6d7ef9dccf3","observation_id":"207d7c4f-9198-4b5e-953b-6564cdcf1c36","resolution":{"observed_at":"2026-08-11T20:57:13.159767Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.143590Z","title":"Useek: Unsupervised se (3)-equivariant 3d keypoints for generalizable manipulation","venue":null,"work_id":"8e5db7e0-bff9-43a2-8af5-d116c6ff6667","year":2023},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":75,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.514308Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:69bb935081903cdf1acdbd0e0a73f9cc64f79dc41dbbd27f18e8f83393a278a3","observation_id":"729543cd-de6a-4501-af75-444d1cb0b9fb","resolution":{"observed_at":"2026-08-11T20:57:13.147757Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.03908","last_updated":"2023-06-06T17:59:51Z","snapshot_observed_at":"2026-08-18T01:10:59.696873Z","submitted_at":"2023-06-06T17:59:51Z","title":"SAM3D: Segment Anything in 3D Scenes","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.03908","snapshot_observed_at":"2026-08-11T20:57:12.518282Z","title":"Sam3d: Segment anything in 3d scenes, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":76,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.518282Z"},"links":{"cited_paper":"/paper/2306.03908","citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:7a854513ef4ba1bbe2af601c4fbb03b9bb985112b9d4da5bfd77b65fcb21504a","observation_id":"3b421402-aef1-4763-90da-5deeeb77256d","resolution":{"observed_at":"2026-08-11T20:57:12.518282Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15815","last_updated":"2024-10-23T05:32:34Z","snapshot_observed_at":"2026-08-16T13:32:03.635999Z","submitted_at":"2024-07-22T17:29:02Z","title":"Learning to Manipulate Anywhere: A Visual Generalizable Framework For Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.15815","snapshot_observed_at":"2026-08-11T20:57:12.521885Z","title":"Learning to manipulate anywhere: A visual generalizable framework for reinforcement learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":77,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.521885Z"},"links":{"cited_paper":"/paper/2407.15815","citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:8d10f10bc5e6f48ad405a3b17be032808383f903f331751088805e920f5eb107","observation_id":"807a81e8-a895-4c32-ae7b-74a84e5ab0a8","resolution":{"observed_at":"2026-08-11T20:57:12.521885Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.131570Z","title":"3d diffusion policy: Generalizable visuomotor policy learning via simple 3d representations","venue":null,"work_id":"531b8a60-d7d4-4e53-bee8-9dfc6622bddb","year":2024},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":78,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.526260Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:f8fef2330ce1d16f2f7f2302d3c4b5f3de5f16a46eb88dc6104cac3bbca4d1b1","observation_id":"68b5c915-1dc3-4c85-b7ad-58252d46add5","resolution":{"observed_at":"2026-08-11T20:57:13.136403Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.119858Z","title":"Corrnet3d: Unsupervised end-to-end learning of dense correspondence for 3d point clouds","venue":null,"work_id":"1197868a-8b09-4634-b797-ca96806576b1","year":2021},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":79,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.529795Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:d7c85160c9db2638e07c0c27b1332504b883bce1784ab6300b07cc06f87b4f66","observation_id":"0f23afae-b05e-4f84-b236-ec2826678d94","resolution":{"observed_at":"2026-08-11T20:57:13.124016Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:12.532969Z","title":"A tale of two features: Stable diffusion complements dino for zero-shot semantic correspondence","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":80,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.532969Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:61dcf8333d274e1f660e72f3aefb9f846eb7fd7c5a86e44ba105a2e84886ff9e","observation_id":"bc57cd66-5384-47f5-98fb-0814972a898e","resolution":{"observed_at":"2026-08-11T20:57:12.532969Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.101230Z","title":"Telling left from right: Identifying geometry-aware semantic correspondence","venue":null,"work_id":"cd775764-2af5-42dd-86bb-c2aa6b71cbb5","year":2024},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":81,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.536137Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:a53be6fcc7021b473a1d915d9aad5d032d80df360eec11d8225d126e862c4731","observation_id":"12cf4bdf-0982-46a1-be5d-d88e11f0a809","resolution":{"observed_at":"2026-08-11T20:57:13.104953Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:57:13.089713Z","title":null,"venue":null,"work_id":"14775393-467e-4026-9b19-b3bbe6ce4c16","year":2017},"citing_paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo","version":1},"reference_index":82,"source":"arxiv_source","source_observed_at":"2026-08-11T20:57:12.539343Z"},"links":{"citing_paper":"/paper/2412.05268"},"observation_digest":"sha256:509ea671970dda33d12ae30132c03589ae1f564a7326c57a3c654b93bfb3f6da","observation_id":"1c2f59ff-9b26-484b-8812-2dbf065b38af","resolution":{"observed_at":"2026-08-11T20:57:13.093636Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.05268","last_updated":"2024-12-06T18:55:09Z","latest_version":1,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-13T09:52:42.403876Z","submitted_at":"2024-12-06T18:55:09Z","title":"DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo"},"reference_resolution":{"displayed":81,"state_counts":{"malformed_identifier":2,"metadata_mismatch":2,"parse_uncertain":0,"unresolved":32,"verified_exact":3,"verified_fuzzy":42},"total_outbound_references":81},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 81 of 81 outbound references and 9 inbound Pith citation observations for arXiv:2412.05268."}