{"as_of":"2026-08-22T12:36:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:23d3de0d84b53db4a803e9e7211e39c3f3571ab00b284c0cacd962545b29e490","coverage":[{"denominator":47,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":47,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-02T18:08:53.268268Z","state":"measured"},{"denominator":48,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":48,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T00:49:38.215194Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-08T00:49:38.734972Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"cited_work":{"arxiv_id":"2603.16103","doi":null,"metadata_source":"pith","pith_arxiv_id":"2603.16103","snapshot_observed_at":"2026-08-08T00:49:38.734972Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","venue":"cs.CV","work_id":"c5c87884-d823-44ea-9703-5ca5d6ffd238","year":2026},"citing_paper":{"arxiv_id":"2608.05704","last_updated":"2026-08-06T07:46:49Z","snapshot_observed_at":"2026-08-20T02:06:38.661550Z","submitted_at":"2026-08-06T07:46:49Z","title":"G$^2$ARD-GS: Geometry-Guided Anchor-Regularized Gaussian Splatting Distillation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-08T00:49:38.215194Z"},"links":{"cited_paper":"/paper/2603.16103","citing_paper":"/paper/2608.05704"},"observation_digest":"sha256:07bea2ca06e17a7933bd49a5fe6ad20b42a50c0d99e111c0c0a98bb2f7f358a3","observation_id":"284ddb7c-922d-4531-821e-f88034e112f9","resolution":{"observed_at":"2026-08-08T00:49:38.740979Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2603.16103/citation-record","integrity":"/paper/2603.16103/integrity","json":"/paper/2603.16103/citation-record.json","paper":"/paper/2603.16103"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.19457","last_updated":"2025-02-26T08:51:21Z","snapshot_observed_at":"2026-08-20T17:33:37.312777Z","submitted_at":"2025-02-26T08:51:21Z","title":"Compression in 3D Gaussian Splatting: A Survey of Methods, Trends, and Future Directions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.19457","snapshot_observed_at":"2026-08-02T18:08:47.458412Z","title":"arXiv preprint arXiv:2502.19457 (2025)","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:47.458412Z"},"links":{"cited_paper":"/paper/2502.19457","citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:b3eae9efc92d4c7805a4cbe4b6bd137edbdd0b25f8c996b416001a3bc6ca5e4c","observation_id":"98d8f318-818b-4893-b5db-03d798dd5809","resolution":{"observed_at":"2026-08-02T18:08:47.458412Z","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":"2026-08-02T18:08:47.544794Z","title":"Computer Graphics Forum (2025)","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:47.544794Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:e9a516f14e66bbeeefebd7905886597c704526e43cd370b9d729134108d02e0d","observation_id":"70aab7b7-c9e5-45f1-a593-4f0ca21a54a5","resolution":{"observed_at":"2026-08-02T18:08:47.544794Z","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":"2026-08-02T18:08:47.693807Z","title":"Journal of machine learning research6(Oct), 1705–1749 (2005)","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:47.693807Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:b09d8959ac938427eaaf2242e80303fa230197baf2ac1621c23d407c262ca117","observation_id":"c9c90259-0bc0-4b13-be34-addae1015139","resolution":{"observed_at":"2026-08-02T18:08:47.693807Z","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":"2026-08-02T18:08:47.848812Z","title":"In: CVPR (2022)","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:47.848812Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:eac561771776e999f9b9c84bf7f44e26e7ccf178246f1d099dcae8c8f76a599c","observation_id":"e954a1b2-fa7c-448c-92c5-88162edd9a78","resolution":{"observed_at":"2026-08-02T18:08:47.848812Z","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":"2026-08-02T18:08:47.986662Z","title":"In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:47.986662Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:396dc5baf14d362fb4a95af069aa569558ce091b9a70d1b3082881ab9d120d93","observation_id":"2dbdf3de-0b7f-4c85-82d0-5c0c26151bf1","resolution":{"observed_at":"2026-08-02T18:08:47.986662Z","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":"2026-08-02T18:08:48.102165Z","title":"In: ICCV (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:48.102165Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:ae8d680149e27db332f744fa4053fa76ec137f30d6183c57dca72ae093b0850f","observation_id":"8e1abb0c-1581-4809-9eda-056ea428ff37","resolution":{"observed_at":"2026-08-02T18:08:48.102165Z","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":"2026-08-02T18:08:48.260845Z","title":"USSR computational mathematics and mathematical physics7(3), 200–217 (1967)","venue":null,"work_id":null,"year":1967},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:48.260845Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:d2dd81d0c59520c44654c9102f1da45b737db6f5a0b899c04e3d5be0eee12b67","observation_id":"6f2e197d-3501-4471-8284-68ec03c0bce5","resolution":{"observed_at":"2026-08-02T18:08:48.260845Z","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":"2026-08-02T18:08:48.424828Z","title":"In: European Conference on Computer Vision","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:48.424828Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:e556948fc748e8cb72f856aab29063fc288bd71755d35730e758b1c5c5bb9ed1","observation_id":"a2df1446-b963-47cb-8008-485608d3f591","resolution":{"observed_at":"2026-08-02T18:08:48.424828Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.12255","last_updated":"2025-02-11T13:03:27Z","snapshot_observed_at":"2026-08-20T13:07:16.388545Z","submitted_at":"2025-01-21T16:23:05Z","title":"HAC++: Towards 100X Compression of 3D Gaussian Splatting","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.12255","snapshot_observed_at":"2026-08-02T18:08:48.601399Z","title":"arXiv preprint arXiv:2501.12255 (2025)","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:48.601399Z"},"links":{"cited_paper":"/paper/2501.12255","citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:924890b3938fb12f9a1265932d832aa0866815ea558cc7df65b833b59da5eac3","observation_id":"74cf11b1-e708-4b23-b7b1-6f2844bbfc26","resolution":{"observed_at":"2026-08-02T18:08:48.601399Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.11247","last_updated":"2026-05-13T03:31:24Z","snapshot_observed_at":"2026-08-06T04:07:06.773895Z","submitted_at":"2024-03-17T15:41:35Z","title":"Compact 3D Gaussian Splatting For Dense Visual SLAM","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.11247","snapshot_observed_at":"2026-08-02T18:08:48.752297Z","title":"arXiv preprint arXiv:2403.11247 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:48.752297Z"},"links":{"cited_paper":"/paper/2403.11247","citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:9858b27da3614b78faea39119bfcd5a85b7aef5f36dfa46766695ae6cca9ad60","observation_id":"9e640ebe-3962-4b88-ae3b-e569c2e283da","resolution":{"observed_at":"2026-08-02T18:08:48.752297Z","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":"2026-08-02T18:08:48.887059Z","title":"In: Pro- ceedings of the ninth ACM SIGKDD international conference on Knowledge dis- covery and data mining","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:48.887059Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:a722465e0f87ac173f416e6d68ed05838d5f4ea2e4f4ff3165fb3aababe35d50","observation_id":"479fb400-88f3-44c5-a67f-46665f983695","resolution":{"observed_at":"2026-08-02T18:08:48.887059Z","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":"2026-08-02T18:08:49.071901Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:49.071901Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:0d7bc59a4730b904597dac4ea78eeffed41695076abac3327a7edcec19dd890b","observation_id":"a76dd784-0c55-483d-a4be-3af1c8b6e722","resolution":{"observed_at":"2026-08-02T18:08:49.071901Z","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":"2026-08-02T18:08:49.263186Z","title":"Advances in neural information processing systems37, 140138–140158 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:49.263186Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:3f7bdda586b1cc4e1a156d503530fe3c79afd9ee47ffb71139b32122b533b09e","observation_id":"5c05260a-013d-4ab8-8d7d-d9121f99f4cc","resolution":{"observed_at":"2026-08-02T18:08:49.263186Z","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":"2026-08-02T18:08:49.439262Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:49.439262Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:b69759db80fcef777376f805a54a2a08e3a3c6ec4fa5fc64b43048a6fa9fbb86","observation_id":"54ce9b60-40c6-4872-9f01-99b945c18358","resolution":{"observed_at":"2026-08-02T18:08:49.439262Z","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":"2026-08-02T18:08:49.523753Z","title":"In: European Conference on Computer Vision","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:49.523753Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:36f039b3835f02c6906a9567c8ab477c506ca50cf4ed8cefb0f123f927790cfc","observation_id":"239b0692-bce8-4411-aa2a-51d8c4269e94","resolution":{"observed_at":"2026-08-02T18:08:49.523753Z","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":"2026-08-02T18:08:49.630431Z","title":"ECCV (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:49.630431Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:4caae441e80cac487c1d86447e3d5f9ceaa2d7d840669c061873677096cf8195","observation_id":"7459927a-d71b-4b9b-bec0-28fd349bdb07","resolution":{"observed_at":"2026-08-02T18:08:49.630431Z","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":"2026-08-02T18:08:49.728963Z","title":"In: Proceedings of the Computer Vision and Pattern Recognition Conference","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:49.728963Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:7fe3adef16d042861562546c90275d0c469facafcf2d91151f5aa22ff4cd8379","observation_id":"1fe5cd1e-2716-4d66-90e0-a4d28e02a8f3","resolution":{"observed_at":"2026-08-02T18:08:49.728963Z","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":"2026-08-02T18:08:49.888834Z","title":"ACM Transactions on Graphics (ToG)37(6), 1–15 (2018)","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:49.888834Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:ff0af729762cd62deaa16d18e1560e7629158e19e2843ac0df1a357ef745a98a","observation_id":"a58aa233-b26b-458c-a6d1-1a4a93715514","resolution":{"observed_at":"2026-08-02T18:08:49.888834Z","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":"2026-08-02T18:08:49.977145Z","title":"In: SIGGRAPH (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:49.977145Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:fe9388b45bf5171ce5644f5ac932becefa2fb892d913e281be5df35ed49d9841","observation_id":"34fc4bc2-cb52-410b-9b5a-1c71a7b4f4e9","resolution":{"observed_at":"2026-08-02T18:08:49.977145Z","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":"2026-08-02T18:08:50.068798Z","title":"In: SIGGRAPH (2023)","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:50.068798Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:24d9a040899b732111ad77f5bd2e2764d18aac21b92c3e2cdd2323dc750e386c","observation_id":"f2d80bbd-9d16-47d3-b7e0-4e5c259fbb53","resolution":{"observed_at":"2026-08-02T18:08:50.068798Z","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":"2026-08-02T18:08:50.185160Z","title":"ACM Transactions on Graphics (ToG)36(4), 1–13 (2017)","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:50.185160Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:0428cbda19cb5dea12a79d25852cd2d57c3722c5b9d9a7de012e0b45e437da54","observation_id":"b062a5b2-dd76-4015-a5d6-0fe0de926899","resolution":{"observed_at":"2026-08-02T18:08:50.185160Z","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":"2026-08-02T18:08:50.382445Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:50.382445Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:ad84066afafd8671182d8f431e2e5c4e9703966f83b73aa7d0f3ad40ce644da0","observation_id":"397c234e-bc29-47ff-82ee-2fc0aec3a932","resolution":{"observed_at":"2026-08-02T18:08:50.382445Z","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":"2026-08-02T18:08:50.512727Z","title":"In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:50.512727Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:ea15d79d25feae2f4b5527caa91a82aab94d0ce515490b1a5c942896cb19ce01","observation_id":"9a25e1c8-cd7a-45ab-8606-c9220be5262d","resolution":{"observed_at":"2026-08-02T18:08:50.512727Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.18473","last_updated":"2025-04-22T13:41:54Z","snapshot_observed_at":"2026-08-18T02:34:02.593554Z","submitted_at":"2024-11-27T16:08:59Z","title":"HEMGS: A Hybrid Entropy Model for 3D Gaussian Splatting Data Compression","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.18473","snapshot_observed_at":"2026-08-02T18:08:50.652903Z","title":"arXiv preprint arXiv:2411.18473 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:50.652903Z"},"links":{"cited_paper":"/paper/2411.18473","citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:1d40622e3789795d67d2e7b4d199bda6584be4cae230b5b8aa11fcb4321bcdca","observation_id":"c1c10182-e648-471b-92fc-a55acbbd7eaf","resolution":{"observed_at":"2026-08-02T18:08:50.652903Z","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":"2026-08-02T18:08:50.771547Z","title":"arXiv preprint arXiv:2510.03312 (2025)","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:50.771547Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:9cd1099772cc23bb030c49214ddae307f5d3d257e34e185638fae72fd00aef03","observation_id":"01967d8d-1833-4e81-ab45-7ed146768ca8","resolution":{"observed_at":"2026-08-02T18:08:50.771547Z","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":"2026-08-02T18:08:50.852485Z","title":"In: Proceedings of the Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:50.852485Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:d19dfbb4288247a6898a94e278d13f620eb6bc2eb372de604ddfda9a084cbbbe","observation_id":"7ba56d4c-7032-4ede-9878-1ba5318c9ecd","resolution":{"observed_at":"2026-08-02T18:08:50.852485Z","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":"2026-08-02T18:08:50.967064Z","title":"In: SIGGRAPH Asia 2024 Conference Papers","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:50.967064Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:3ff4579409294c92a5d7f95252842a1771fdc520b43529da07c44b204869deed","observation_id":"d30ecadc-65a5-4746-af35-203cb78a575b","resolution":{"observed_at":"2026-08-02T18:08:50.967064Z","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":"2026-08-02T18:08:51.053967Z","title":"In: ECCV (2020)","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:51.053967Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:678d59393296aebba853c6b193c87ccc46a433cd780e0bc940f06ecf79004d83","observation_id":"d83c231f-6b6c-40db-a069-9a9da35f326f","resolution":{"observed_at":"2026-08-02T18:08:51.053967Z","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":"2026-08-02T18:08:51.142216Z","title":"In: SIGGRAPH (2022)","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:51.142216Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:76b3d8401b4333e232a376cccb1580d010074cd1183a69cd609779210c03c2ae","observation_id":"2a44ff38-f91e-4b81-9526-6837733c3485","resolution":{"observed_at":"2026-08-02T18:08:51.142216Z","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":"2026-08-02T18:08:51.323106Z","title":"In: Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:51.323106Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:40436a3207843ffd0e306631e3c85e742bdd169e6e6c1b981a9b4faf42313ac7","observation_id":"e6f70f38-0cd5-48ed-87d4-ed24c37ecc39","resolution":{"observed_at":"2026-08-02T18:08:51.323106Z","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":"2026-08-02T18:08:51.495862Z","title":"Proceedings of the ACM on Computer Graphics and Interactive Techniques7(1) (May 2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:51.495862Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:64b21d39816a4effc5fb4c37982fdb7c2f524827f12254ed2bdba7276c8a4b56","observation_id":"a541d08f-ad3d-4ed8-a9fa-9a2994c87f8d","resolution":{"observed_at":"2026-08-02T18:08:51.495862Z","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":"2026-08-02T18:08:51.593782Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:51.593782Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:4abc0efb87f0f11d126701a9cbdaf628aadd76429ff7a9b4c7b58b5e44bef1cc","observation_id":"7e20c4fd-c644-4d28-be28-f3fd8813a9aa","resolution":{"observed_at":"2026-08-02T18:08:51.593782Z","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":"2026-08-02T18:08:51.706157Z","title":"In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:51.706157Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:dc40c320e9e0fe6edd9ee766c2937c01950f3fb1a1800ccb1860d235f92057a8","observation_id":"0241d44d-3c0b-4edb-8f4c-5985518439f2","resolution":{"observed_at":"2026-08-02T18:08:51.706157Z","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":"2026-08-02T18:08:51.798696Z","title":"IEEE Transactions on Aerospace and Electronic Systems43(3), 989–999 (2007) NanoGS 17","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:51.798696Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:1c71014549a38d52e2bc227a8eee1d8c397865019c7efbb06b629876671ba3a3","observation_id":"d7ed8279-886f-4048-bda7-96e8c4635aa7","resolution":{"observed_at":"2026-08-02T18:08:51.798696Z","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":"2026-08-02T18:08:51.974260Z","title":"In: ICLR (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:51.974260Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:f0254c8dbc3932b165b0f501d42c5359baf1b020038b7ba1367dd7fd8e9fef1c","observation_id":"76cee472-9172-4fda-91d5-66171249dda2","resolution":{"observed_at":"2026-08-02T18:08:51.974260Z","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":"2026-08-02T18:08:52.135314Z","title":"In: Computer Graphics Forum","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:52.135314Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:66f3ff18fdebb11fa3d99a58dcabae369886b3208dcc44addac4d6e5cb053c55","observation_id":"518c4459-fafa-4f30-832e-c942ed370a5c","resolution":{"observed_at":"2026-08-02T18:08:52.135314Z","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":"2026-08-02T18:08:52.239166Z","title":"In: Advances in Neural Information Processing Systems (NeurIPS 2025) (2025)","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:52.239166Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:6fef899d6231c071d4bf14f41a7953c2ea090d3a7846d98bf32ccffe59a4841d","observation_id":"881f2bc2-a29d-4278-bbe8-a86e58a14cdf","resolution":{"observed_at":"2026-08-02T18:08:52.239166Z","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":"2026-08-02T18:08:52.385867Z","title":"Advances in neural information processing systems37, 51532–51551 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:52.385867Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:7c45d9602f6c99d45b57e2d2b37414cf33ad6fd58e87768f50bf816bcfbf28c0","observation_id":"b45417a6-0307-4dfb-b516-f60f40f4942c","resolution":{"observed_at":"2026-08-02T18:08:52.385867Z","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":"2026-08-02T18:08:52.493756Z","title":"In: CVPR (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:52.493756Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:7c139b0832df2b0502e1608e6907bff6f79d1522ddfc03852f7d4bd52232dda2","observation_id":"20589fbf-1faf-493d-8ab8-3ab4638dae58","resolution":{"observed_at":"2026-08-02T18:08:52.493756Z","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":"2026-08-02T18:08:52.626501Z","title":"In: ICLR (2026)","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:52.626501Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:1433dfddbedf4a69adc4e267d8c5fa9b10c7562d4f6204fb15666a8365ca82e5","observation_id":"c4291ef4-130e-4a4b-a19b-366aa2e97802","resolution":{"observed_at":"2026-08-02T18:08:52.626501Z","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":"2026-08-02T18:08:52.724244Z","title":"In: ICLR (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:52.724244Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:e5325a4600eb59fcb22cdb9fefad871ce92284f601ba82de464194605e20bc4a","observation_id":"563c2b49-53fe-4047-ae6c-df6d836c05a3","resolution":{"observed_at":"2026-08-02T18:08:52.724244Z","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":"2026-08-02T18:08:52.789859Z","title":"In: CVPR (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:52.789859Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:45870370c3226793f0419aa62c0fec2ba0e26c10ce8ea183d0bfc4f98dd2c3a4","observation_id":"72fe8cc9-c00e-4ed2-a4be-5e8f74dc76aa","resolution":{"observed_at":"2026-08-02T18:08:52.789859Z","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":"2026-08-02T18:08:52.852095Z","title":"In: CVPR (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:52.852095Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:9f496559ba252d0f65836fadb3e843a06770e6564aed542fc57b61460ba952c1","observation_id":"2fe346e6-a424-4321-ac13-5134f1ab328a","resolution":{"observed_at":"2026-08-02T18:08:52.852095Z","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":"2026-08-02T18:08:52.961613Z","title":"optimizing-sparsifying","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:52.961613Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:b9770bfa8107fdbcf69ef246f5018f3d9685147acbed8e7200283abce9ec016d","observation_id":"a3b542d6-19b0-4f44-9ddc-ffc6e1b06c52","resolution":{"observed_at":"2026-08-02T18:08:52.961613Z","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":"2026-08-02T18:08:53.064804Z","title":"Advances in Neural Information Processing Systems37, 122434–122457 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:53.064804Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:bf8afdc9986c6ef2940b806558c1d1e3029adc94ad6c10375661f9eca07034f0","observation_id":"e29507fb-4374-42f2-bbc7-647783656c70","resolution":{"observed_at":"2026-08-02T18:08:53.064804Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.17715","last_updated":"2024-12-23T16:45:37Z","snapshot_observed_at":"2026-08-21T10:11:56.091648Z","submitted_at":"2024-12-23T16:45:37Z","title":"GaussianPainter: Painting Point Cloud into 3D Gaussians with Normal Guidance","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.17715","snapshot_observed_at":"2026-08-02T18:08:53.166058Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:53.166058Z"},"links":{"cited_paper":"/paper/2412.17715","citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:23b16c6ab0ddc3dccb39da1dba1736c171e9e64a53d6796b2daba9dc81b1d0a6","observation_id":"692d0600-8610-43f5-9489-6527411a1436","resolution":{"observed_at":"2026-08-02T18:08:53.166058Z","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":"2026-08-02T18:08:53.268268Z","title":"ChatGPT","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-02T18:08:53.268268Z"},"links":{"citing_paper":"/paper/2603.16103"},"observation_digest":"sha256:4559d8f5614715410e86245518b8171f000b1306d475fbc4cd469557a0782fa7","observation_id":"a092b166-554f-4a17-9e6e-c5ad50218a41","resolution":{"observed_at":"2026-08-02T18:08:53.268268Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2603.16103","last_updated":"2026-07-14T18:05:03Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-16T01:17:24.610169Z","submitted_at":"2026-03-17T03:58:02Z","title":"NanoGS: Training-Free Gaussian Splat Simplification"},"reference_resolution":{"displayed":47,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":47,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":47},"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":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 1 inbound Pith citation observation for arXiv:2603.16103."}