{"as_of":"2026-08-14T11:43:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f37a71cd2146ae9aaf2390c5d5e619725a10e2d0af2bb1999cee1098a4c5cf82","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":9,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":9,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+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":"2026-08-14T10:32:34.910029Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":103,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1811.07246","last_updated":"2020-11-09T21:20:22Z","snapshot_observed_at":"2026-08-07T04:37:41.847265Z","submitted_at":"2018-11-17T23:42:13Z","title":"PointConv: Deep Convolutional Networks on 3D Point Clouds","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.07246","snapshot_observed_at":"2026-08-14T10:32:34.910029Z","title":"Pointconv: Deep convolutional networks on 3d point clouds","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.11069","last_updated":"2019-12-02T22:15:26Z","snapshot_observed_at":"2026-08-14T10:23:11.089133Z","submitted_at":"2019-08-29T06:54:46Z","title":"StarNet: Targeted Computation for Object Detection in Point Clouds","version":3},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-14T10:32:34.910029Z"},"links":{"cited_paper":"/paper/1811.07246","citing_paper":"/paper/1908.11069"},"observation_digest":"sha256:d41e35ccb9135fd4fc52df64e0a1a6c83bb3628640cb2527976d30fdc3441148","observation_id":"f366c65c-cc2d-4dae-a74e-9d3abcb8649f","resolution":{"observed_at":"2026-08-14T10:32:34.910029Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1811.07246","last_updated":"2020-11-09T21:20:22Z","snapshot_observed_at":"2026-08-07T04:37:41.847265Z","submitted_at":"2018-11-17T23:42:13Z","title":"PointConv: Deep Convolutional Networks on 3D Point Clouds","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.07246","snapshot_observed_at":"2026-08-11T14:00:01.633209Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.12626","last_updated":"2024-12-17T07:41:06Z","snapshot_observed_at":"2026-08-14T04:05:50.809701Z","submitted_at":"2024-12-17T07:41:06Z","title":"Improving the Transferability of 3D Point Cloud Attack via Spectral-aware Admix and Optimization Designs","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-11T14:00:01.633209Z"},"links":{"cited_paper":"/paper/1811.07246","citing_paper":"/paper/2412.12626"},"observation_digest":"sha256:ca6d02976b55e5e2906244bf4d5b1670ceb4af5431d2728043f6b1975cf0ae9e","observation_id":"38bc305d-6474-414d-a162-6b9f692214e7","resolution":{"observed_at":"2026-08-11T14:00:01.633209Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1811.07246","last_updated":"2020-11-09T21:20:22Z","snapshot_observed_at":"2026-08-07T04:37:41.847265Z","submitted_at":"2018-11-17T23:42:13Z","title":"PointConv: Deep Convolutional Networks on 3D Point Clouds","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.07246","snapshot_observed_at":"2026-08-05T15:27:12.690543Z","title":"PointConv: Deep Convolutional Networks on 3D Point Clouds,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.19909","last_updated":"2025-08-27T14:13:01Z","snapshot_observed_at":"2026-08-06T11:58:51.628386Z","submitted_at":"2025-08-27T14:13:01Z","title":"Integrating SAM Supervision for 3D Weakly Supervised Point Cloud Segmentation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T15:27:12.690543Z"},"links":{"cited_paper":"/paper/1811.07246","citing_paper":"/paper/2508.19909"},"observation_digest":"sha256:dfbc1d36e80d95aa510ce4c7151f111533f283b3860493662d223fe2fba40627","observation_id":"666672ba-af33-4b69-9d57-b50170379067","resolution":{"observed_at":"2026-08-05T15:27:12.690543Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1811.07246","last_updated":"2020-11-09T21:20:22Z","snapshot_observed_at":"2026-08-07T04:37:41.847265Z","submitted_at":"2018-11-17T23:42:13Z","title":"PointConv: Deep Convolutional Networks on 3D Point Clouds","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.07246","snapshot_observed_at":"2026-08-05T12:19:11.858142Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.01746","last_updated":"2025-09-01T20:00:56Z","snapshot_observed_at":"2026-08-11T17:38:50.303053Z","submitted_at":"2025-09-01T20:00:56Z","title":"Fail2Progress: Learning from Real-World Robot Failures with Stein Variational Inference","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-05T12:19:11.858142Z"},"links":{"cited_paper":"/paper/1811.07246","citing_paper":"/paper/2509.01746"},"observation_digest":"sha256:689da913116c18ccc3290ead4c1c61aba395bc0b9bb284d827dd8e185438e7b7","observation_id":"a81b9cb1-6aa6-445e-bf34-0de55ae360aa","resolution":{"observed_at":"2026-08-05T12:19:11.858142Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1811.07246","last_updated":"2020-11-09T21:20:22Z","snapshot_observed_at":"2026-08-07T04:37:41.847265Z","submitted_at":"2018-11-17T23:42:13Z","title":"PointConv: Deep Convolutional Networks on 3D Point Clouds","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.07246","snapshot_observed_at":"2026-08-05T11:11:44.937614Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.03095","last_updated":"2025-09-03T07:51:17Z","snapshot_observed_at":"2026-08-13T11:40:18.828953Z","submitted_at":"2025-09-03T07:51:17Z","title":"TRELLIS-Enhanced Surface Features for Comprehensive Intracranial Aneurysm Analysis","version":1},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-05T11:11:44.937614Z"},"links":{"cited_paper":"/paper/1811.07246","citing_paper":"/paper/2509.03095"},"observation_digest":"sha256:7ffbdc616253a6c8a894cad474f79bf179b830abe96398faf3a5641421f15b41","observation_id":"d767d2fb-8d6e-455f-889a-2b86b123091f","resolution":{"observed_at":"2026-08-05T11:11:44.937614Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1811.07246","last_updated":"2020-11-09T21:20:22Z","snapshot_observed_at":"2026-08-07T04:37:41.847265Z","submitted_at":"2018-11-17T23:42:13Z","title":"PointConv: Deep Convolutional Networks on 3D Point Clouds","version":3},"cited_work":{"arxiv_id":"1811.07246","doi":"10.48550/arxiv.1811.07246","metadata_source":"arxiv_reference","pith_arxiv_id":"1811.07246","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"PointConv: Deep Convolutional Networks on 3D Point Clouds , publisher =","venue":"arXiv (Cornell University)","work_id":"d976ab11-1897-42ce-8c8f-635c994a5613","year":2020},"citing_paper":{"arxiv_id":"2605.02098","last_updated":"2026-05-21T07:27:46Z","snapshot_observed_at":"2026-08-11T00:08:41.535406Z","submitted_at":"2026-05-03T23:36:32Z","title":"From Spherical to Gaussian: A Comparative Analysis of Point Cloud Cropping Strategies in Large-Scale 3D Environments","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-08T19:15:57.936114Z"},"links":{"cited_paper":"/paper/1811.07246","citing_paper":"/paper/2605.02098"},"observation_digest":"sha256:0a80f99b827ffc07a940080343bc37e025947f1406f1d41a1065127cd22522f9","observation_id":"a9a027c9-25ec-47b9-af43-a8161c10568a","resolution":{"observed_at":"2026-05-09T06:00:33.776554Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.07246","last_updated":"2020-11-09T21:20:22Z","snapshot_observed_at":"2026-08-07T04:37:41.847265Z","submitted_at":"2018-11-17T23:42:13Z","title":"PointConv: Deep Convolutional Networks on 3D Point Clouds","version":3},"cited_work":{"arxiv_id":"1811.07246","doi":"10.48550/arxiv.1811.07246","metadata_source":"arxiv_reference","pith_arxiv_id":"1811.07246","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"PointConv: Deep Convolutional Networks on 3D Point Clouds , publisher =","venue":"arXiv (Cornell University)","work_id":"d976ab11-1897-42ce-8c8f-635c994a5613","year":2020},"citing_paper":{"arxiv_id":"2605.02098","last_updated":"2026-05-21T07:27:46Z","snapshot_observed_at":"2026-08-11T00:08:41.535406Z","submitted_at":"2026-05-03T23:36:32Z","title":"From Spherical to Gaussian: A Comparative Analysis of Point Cloud Cropping Strategies in Large-Scale 3D Environments","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-22T09:50:31.499822Z"},"links":{"cited_paper":"/paper/1811.07246","citing_paper":"/paper/2605.02098"},"observation_digest":"sha256:01cf3e0bda79c0e719010dcc934af1dad0830dabb8689a3a68426074392a54c8","observation_id":"6566bdd3-ca6b-478b-88e7-c38976e4a675","resolution":{"observed_at":"2026-05-22T09:51:21.645771Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.07246","last_updated":"2020-11-09T21:20:22Z","snapshot_observed_at":"2026-08-07T04:37:41.847265Z","submitted_at":"2018-11-17T23:42:13Z","title":"PointConv: Deep Convolutional Networks on 3D Point Clouds","version":3},"cited_work":{"arxiv_id":"1811.07246","doi":"10.48550/arxiv.1811.07246","metadata_source":"arxiv_reference","pith_arxiv_id":"1811.07246","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"PointConv: Deep Convolutional Networks on 3D Point Clouds , publisher =","venue":"arXiv (Cornell University)","work_id":"d976ab11-1897-42ce-8c8f-635c994a5613","year":2020},"citing_paper":{"arxiv_id":"2605.29549","last_updated":"2026-05-28T08:02:17Z","snapshot_observed_at":"2026-08-13T13:13:55.954628Z","submitted_at":"2026-05-28T08:02:17Z","title":"Learning Representations from 3D Gaussian Splats","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-29T08:38:34.337340Z"},"links":{"cited_paper":"/paper/1811.07246","citing_paper":"/paper/2605.29549"},"observation_digest":"sha256:57f2f01d296d1dead6a4fa9b96a1e42306eb78d77b149858700aae212cc5e8b0","observation_id":"26ad1551-f55b-4c5d-908b-4b748f787e7f","resolution":{"observed_at":"2026-06-29T08:43:15.404906Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.07246","last_updated":"2020-11-09T21:20:22Z","snapshot_observed_at":"2026-08-07T04:37:41.847265Z","submitted_at":"2018-11-17T23:42:13Z","title":"PointConv: Deep Convolutional Networks on 3D Point Clouds","version":3},"cited_work":{"arxiv_id":"1811.07246","doi":"10.48550/arxiv.1811.07246","metadata_source":"arxiv_reference","pith_arxiv_id":"1811.07246","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"PointConv: Deep Convolutional Networks on 3D Point Clouds , publisher =","venue":"arXiv (Cornell University)","work_id":"d976ab11-1897-42ce-8c8f-635c994a5613","year":2020},"citing_paper":{"arxiv_id":"2606.09432","last_updated":"2026-06-08T12:42:10Z","snapshot_observed_at":"2026-07-06T23:48:48.962930Z","submitted_at":"2026-06-08T12:42:10Z","title":"Graph Mamba Operator: A Latent Simulator for Interacting Particle Systems","version":1},"reference_index":75,"source":"arxiv_source","source_observed_at":"2026-06-27T17:07:27.417845Z"},"links":{"cited_paper":"/paper/1811.07246","citing_paper":"/paper/2606.09432"},"observation_digest":"sha256:96bd3974a03f3bfd5ced4c28576c036182c2e3d4c55139e7a9109a3807173372","observation_id":"88c74f98-1457-4580-aec4-cd9eb316896c","resolution":{"observed_at":"2026-06-27T17:11:05.555740Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1811.07246/citation-record","integrity":"/paper/1811.07246/integrity","json":"/paper/1811.07246/citation-record.json","paper":"/paper/1811.07246"},"outbound":[],"paper":{"arxiv_id":"1811.07246","last_updated":"2020-11-09T21:20:22Z","latest_version":3,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-07T04:37:41.847265Z","submitted_at":"2018-11-17T23:42:13Z","title":"PointConv: Deep Convolutional Networks on 3D Point Clouds"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:1811.07246."}