{"as_of":"2026-08-18T00:05:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:25acaa10729f52af0906d89dc55b679d5c97c8cc8f5c9c26f5653cbc724ab6f9","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":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T21:22:17.649284Z","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":"2026-05-14T23:07:42.833248Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2302.14007","last_updated":"2023-09-25T17:22:39Z","snapshot_observed_at":"2026-08-16T15:52:20.904732Z","submitted_at":"2023-02-27T17:56:18Z","title":"Joint-MAE: 2D-3D Joint Masked Autoencoders for 3D Point Cloud Pre-training","version":3},"cited_work":{"arxiv_id":"2302.14007","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2302.14007","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2302.14007 , year=","venue":null,"work_id":"850d8226-ea4a-4fc0-8d34-a9c5cbc93a2d","year":null},"citing_paper":{"arxiv_id":"2303.16199","last_updated":"2024-09-18T23:54:36Z","snapshot_observed_at":"2026-08-13T10:46:50.834601Z","submitted_at":"2023-03-28T17:59:12Z","title":"LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention","version":3},"reference_index":189,"source":"arxiv_source","source_observed_at":"2026-05-14T23:07:42.245641Z"},"links":{"cited_paper":"/paper/2302.14007","citing_paper":"/paper/2303.16199"},"observation_digest":"sha256:9c88edde0595207169ae47a7df5673aeab1320a5bd8dc67abe65605eeacfa75d","observation_id":"e59591fe-4eb0-4c56-973c-920e41ffca91","resolution":{"observed_at":"2026-05-14T23:07:42.836122Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.14007","last_updated":"2023-09-25T17:22:39Z","snapshot_observed_at":"2026-08-16T15:52:20.904732Z","submitted_at":"2023-02-27T17:56:18Z","title":"Joint-MAE: 2D-3D Joint Masked Autoencoders for 3D Point Cloud Pre-training","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.14007","snapshot_observed_at":"2026-08-10T21:22:17.649284Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.05095","last_updated":"2025-01-09T09:21:09Z","snapshot_observed_at":"2026-08-16T18:47:02.073705Z","submitted_at":"2025-01-09T09:21:09Z","title":"Advancing ALS Applications with Large-Scale Pre-training: Dataset Development and Downstream Assessment","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-10T21:22:17.649284Z"},"links":{"cited_paper":"/paper/2302.14007","citing_paper":"/paper/2501.05095"},"observation_digest":"sha256:8dc85eb879370f2842d163571c642062f88e5c1198a9c0e5192b61dc8e844829","observation_id":"47fd427e-1f5c-4d50-8e64-e91a40a52417","resolution":{"observed_at":"2026-08-10T21:22:17.649284Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.14007","last_updated":"2023-09-25T17:22:39Z","snapshot_observed_at":"2026-08-16T15:52:20.904732Z","submitted_at":"2023-02-27T17:56:18Z","title":"Joint-MAE: 2D-3D Joint Masked Autoencoders for 3D Point Cloud Pre-training","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.14007","snapshot_observed_at":"2026-08-06T22:31:38.846248Z","title":"Joint-mae: 2d-3d joint masked au- toencoders for 3d point cloud pre-training","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.21541","last_updated":"2025-07-30T06:48:41Z","snapshot_observed_at":"2026-08-17T13:20:47.619097Z","submitted_at":"2025-06-26T17:58:05Z","title":"StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning","version":3},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T22:31:38.846248Z"},"links":{"cited_paper":"/paper/2302.14007","citing_paper":"/paper/2506.21541"},"observation_digest":"sha256:ae0a029d8f13ff86cb4eb39e8a2aa03075fb7d935d3dc2c4f878ebee955a2c87","observation_id":"b66b8a6a-133d-4cbb-8cfd-c6d6bc5fe5eb","resolution":{"observed_at":"2026-08-06T22:31:38.846248Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.14007","last_updated":"2023-09-25T17:22:39Z","snapshot_observed_at":"2026-08-16T15:52:20.904732Z","submitted_at":"2023-02-27T17:56:18Z","title":"Joint-MAE: 2D-3D Joint Masked Autoencoders for 3D Point Cloud Pre-training","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.14007","snapshot_observed_at":"2026-08-06T18:10:13.986793Z","title":"Joint-mae: 2d-3d joint masked au- toencoders for 3d point cloud pre-training.arXiv preprint arXiv:2302.14007, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.09102","last_updated":"2025-07-12T01:20:07Z","snapshot_observed_at":"2026-08-06T18:02:05.774630Z","submitted_at":"2025-07-12T01:20:07Z","title":"Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T18:10:13.986793Z"},"links":{"cited_paper":"/paper/2302.14007","citing_paper":"/paper/2507.09102"},"observation_digest":"sha256:cbe2b90e21abbcd97269dda9f9eafc147be6d6e7fe6db61e2446b71663b4521a","observation_id":"3b893064-dba0-4fe3-9606-5cc310dcbf84","resolution":{"observed_at":"2026-08-06T18:10:13.986793Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.14007","last_updated":"2023-09-25T17:22:39Z","snapshot_observed_at":"2026-08-16T15:52:20.904732Z","submitted_at":"2023-02-27T17:56:18Z","title":"Joint-MAE: 2D-3D Joint Masked Autoencoders for 3D Point Cloud Pre-training","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.14007","snapshot_observed_at":"2026-08-05T12:47:49.159320Z","title":"Joint-mae: 2d-3d joint masked au- toencoders for 3d point cloud pre-training","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.01250","last_updated":"2025-09-01T08:42:17Z","snapshot_observed_at":"2026-08-15T07:52:07.911656Z","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":18,"source":"pdf_text","source_observed_at":"2026-08-05T12:47:49.159320Z"},"links":{"cited_paper":"/paper/2302.14007","citing_paper":"/paper/2509.01250"},"observation_digest":"sha256:c2dc4a5e32f881c628692b70cf5c7aa359549fa39d62528079d00822111170bf","observation_id":"b72c5d02-8db7-4219-a092-f0e4ea5d1041","resolution":{"observed_at":"2026-08-05T12:47:49.159320Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2302.14007/citation-record","integrity":"/paper/2302.14007/integrity","json":"/paper/2302.14007/citation-record.json","paper":"/paper/2302.14007"},"outbound":[],"paper":{"arxiv_id":"2302.14007","last_updated":"2023-09-25T17:22:39Z","latest_version":3,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-16T15:52:20.904732Z","submitted_at":"2023-02-27T17:56:18Z","title":"Joint-MAE: 2D-3D Joint Masked Autoencoders for 3D Point Cloud Pre-training"},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2302.14007."}