{"as_of":"2026-08-09T18:16:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2eb6c1edc1dc32b23979c228c08b96c7eb5dd2f1d84485246c9485dc1a4feaa3","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":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:31:04.494724Z","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":"2026-05-13T20:28:13.968090Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2212.08320","last_updated":"2023-02-02T07:26:26Z","snapshot_observed_at":"2026-08-04T11:34:39.486392Z","submitted_at":"2022-12-16T07:46:53Z","title":"Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.08320","snapshot_observed_at":"2026-08-07T14:31:04.494724Z","title":"Autoencoders as cross-modal teachers: Can pretrained 2d image transformers help 3d representation learning? arXiv preprint arXiv:2212.08320, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.18819","last_updated":"2025-05-24T18:26:30Z","snapshot_observed_at":"2026-08-09T01:59:38.626593Z","submitted_at":"2025-05-24T18:26:30Z","title":"Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-07T14:31:04.494724Z"},"links":{"cited_paper":"/paper/2212.08320","citing_paper":"/paper/2505.18819"},"observation_digest":"sha256:ca303e0d35a5b91607cceb45fb399f5acbbd4ba6be3646ff4e38f2e7cdd53a48","observation_id":"75a276ea-305f-434d-bb40-67d08cba74c7","resolution":{"observed_at":"2026-08-07T14:31:04.494724Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.08320","last_updated":"2023-02-02T07:26:26Z","snapshot_observed_at":"2026-08-04T11:34:39.486392Z","submitted_at":"2022-12-16T07:46:53Z","title":"Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.08320","snapshot_observed_at":"2026-08-07T04:40:58.997365Z","title":"Autoen- coders as cross-modal teachers: Can pretrained 2d image transformers help 3d representation learning? arXiv preprint arXiv:2212.08320, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.09952","last_updated":"2025-06-11T17:23:21Z","snapshot_observed_at":"2026-08-09T18:06:17.997902Z","submitted_at":"2025-06-11T17:23:21Z","title":"UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T04:40:58.997365Z"},"links":{"cited_paper":"/paper/2212.08320","citing_paper":"/paper/2506.09952"},"observation_digest":"sha256:dc5f6b7fa11553b96d7d5fd457aed5182d5e51c56bfcbb4ada5db36c623691cd","observation_id":"813ae369-a2a8-4b6b-b832-706d0cad5a72","resolution":{"observed_at":"2026-08-07T04:40:58.997365Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.08320","last_updated":"2023-02-02T07:26:26Z","snapshot_observed_at":"2026-08-04T11:34:39.486392Z","submitted_at":"2022-12-16T07:46:53Z","title":"Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.08320","snapshot_observed_at":"2026-08-06T22:26:20.192816Z","title":"Autoencoders as cross-modal teachers: Can pretrained 2d image transformers help 3d representation learning? arXiv preprint arXiv:2212.08320, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.21724","last_updated":"2025-06-26T19:17:10Z","snapshot_observed_at":"2026-08-09T10:38:25.225141Z","submitted_at":"2025-06-26T19:17:10Z","title":"Asymmetric Dual Self-Distillation for 3D Self-Supervised Representation Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T22:26:20.192816Z"},"links":{"cited_paper":"/paper/2212.08320","citing_paper":"/paper/2506.21724"},"observation_digest":"sha256:41516f875abf608be1c8aa37ba8306f57d3c53916f5dc75dfff11c2ce1fcdfe8","observation_id":"1ff89e71-7df1-44ca-92f7-f9e061cac7d9","resolution":{"observed_at":"2026-08-06T22:26:20.192816Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.08320","last_updated":"2023-02-02T07:26:26Z","snapshot_observed_at":"2026-08-04T11:34:39.486392Z","submitted_at":"2022-12-16T07:46:53Z","title":"Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.08320","snapshot_observed_at":"2026-08-05T12:47:49.134477Z","title":"Autoen- coders as cross-modal teachers: Can pretrained 2d image transformers help 3d representation learning? arXiv preprint arXiv:2212.08320, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.01250","last_updated":"2025-09-01T08:42:17Z","snapshot_observed_at":"2026-08-09T12:28:13.038801Z","submitted_at":"2025-09-01T08:42:17Z","title":"Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T12:47:49.134477Z"},"links":{"cited_paper":"/paper/2212.08320","citing_paper":"/paper/2509.01250"},"observation_digest":"sha256:2da35a1d671171d61e8521afc938391e7d635a0f30addb1142b6a87db90b607b","observation_id":"0e5602c2-f5ed-4eb0-a700-b6332f490aeb","resolution":{"observed_at":"2026-08-05T12:47:49.134477Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.08320","last_updated":"2023-02-02T07:26:26Z","snapshot_observed_at":"2026-08-04T11:34:39.486392Z","submitted_at":"2022-12-16T07:46:53Z","title":"Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?","version":2},"cited_work":{"arxiv_id":"2212.08320","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2212.08320","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"S., Geiger, M., K ¨ohler, J., and Welling, M","venue":null,"work_id":"d841bd68-f5bb-46b0-9eab-8ba26b05e18e","year":2018},"citing_paper":{"arxiv_id":"2604.02845","last_updated":"2026-04-03T08:01:51Z","snapshot_observed_at":"2026-08-02T13:19:20.548556Z","submitted_at":"2026-04-03T08:01:51Z","title":"Deformation-based In-Context Learning for Point Cloud Understanding","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-13T19:57:08.451707Z"},"links":{"cited_paper":"/paper/2212.08320","citing_paper":"/paper/2604.02845"},"observation_digest":"sha256:e78c2706fe2a132ba8ad8d436f27b6487c343ca6e66c8c433d16fc4eff768baa","observation_id":"b1a932d2-aa13-4538-84ae-8f70788c3b8b","resolution":{"observed_at":"2026-05-13T19:58:12.017795Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2212.08320","last_updated":"2023-02-02T07:26:26Z","snapshot_observed_at":"2026-08-04T11:34:39.486392Z","submitted_at":"2022-12-16T07:46:53Z","title":"Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?","version":2},"cited_work":{"arxiv_id":"2212.08320","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2212.08320","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"S., Geiger, M., K ¨ohler, J., and Welling, M","venue":null,"work_id":"d841bd68-f5bb-46b0-9eab-8ba26b05e18e","year":2018},"citing_paper":{"arxiv_id":"2604.03334","last_updated":"2026-04-03T06:02:29Z","snapshot_observed_at":"2026-07-06T22:52:29.705312Z","submitted_at":"2026-04-03T06:02:29Z","title":"Bridging the Dimensionality Gap: A Taxonomy and Survey of 2D Vision Model Adaptation for 3D Analysis","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-13T20:26:27.298974Z"},"links":{"cited_paper":"/paper/2212.08320","citing_paper":"/paper/2604.03334"},"observation_digest":"sha256:ffa3083967ba3995c30e76b5481aa69ff02d00619451696ed4e15ac6435b34bd","observation_id":"26cf73f1-401b-4332-9900-3a195192e437","resolution":{"observed_at":"2026-05-13T20:28:13.969779Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2212.08320/citation-record","integrity":"/paper/2212.08320/integrity","json":"/paper/2212.08320/citation-record.json","paper":"/paper/2212.08320"},"outbound":[],"paper":{"arxiv_id":"2212.08320","last_updated":"2023-02-02T07:26:26Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-04T11:34:39.486392Z","submitted_at":"2022-12-16T07:46:53Z","title":"Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?"},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2212.08320."}