{"as_of":"2026-08-18T14:16:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2f8b3a22b3c37441408d88eea7a50343a57fb58f5484938ac6798ae291fdbc92","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":"2026-08-11T13:08:07.491975Z","state":"measured"},{"denominator":81,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":81,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2412.13502/citation-record","integrity":"/paper/2412.13502/integrity","json":"/paper/2412.13502/citation-record.json","paper":"/paper/2412.13502"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T13:08:07.305491Z","title":"Pointnet: Deep learning on point sets for 3d classification and segmentation","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.305491Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:5dd8ad3fcb51907d4c24c719a0af29c0a14ce9ef604005dc98460b58b12f848c","observation_id":"1ecb3bda-2781-4a3d-ad96-52d10da6626f","resolution":{"observed_at":"2026-08-11T13:08:07.305491Z","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":"2026-08-11T13:08:07.309082Z","title":"Dynamic graph cnn for learning on point clouds","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.309082Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:50904d63fea83b59633735d216bf84ad03f5a7432898b38641d8d62bc5aa8b9d","observation_id":"acbbb937-3bbc-489c-a9c6-b1a4308b3b23","resolution":{"observed_at":"2026-08-11T13:08:07.309082Z","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":"2026-08-11T13:08:08.106373Z","title":"PointCNN: Convolution on x-transformed points","venue":null,"work_id":"ba341e91-019c-4c30-b64b-07d81aefb76b","year":2018},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.312742Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:d6cb49049089d462eb09f7b8ce97a64316966255a2b99e550b26ef653ebed275","observation_id":"dae8364e-afb9-4eb3-81ca-b758d23a5dc7","resolution":{"observed_at":"2026-08-11T13:08:08.108741Z","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":"2026-08-11T13:08:08.099477Z","title":"Spherical kernel for efficient graph convolution on 3d point clouds","venue":null,"work_id":"9d1ac26b-93d6-4d86-8a11-cb06e3ff8e83","year":2020},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.316588Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:2a3acd539635557a00b7fb8e8d765874572e877013410dd9deaecdb8a4f37766","observation_id":"49239577-e2f2-4f2f-b88f-dbc09dc86d8b","resolution":{"observed_at":"2026-08-11T13:08:08.101923Z","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":"2026-08-11T13:08:08.092927Z","title":"MeshCNN: a network with an edge","venue":null,"work_id":"7b43497c-2317-45f1-ad31-62883787a817","year":2019},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.320036Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:26019f0fb0b8105dca22ce2309631c65286020e79d406da7e3fb4b73806f7425","observation_id":"290117be-32eb-4f27-911a-8e14a83b124c","resolution":{"observed_at":"2026-08-11T13:08:08.095295Z","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":"2026-08-11T13:08:08.085085Z","title":"Subdivision-based mesh convolution networks","venue":null,"work_id":"b692011e-9240-4a82-bde8-5b7a1656e25c","year":2022},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.323123Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:f7c474b4bcd9ac120ce80b425fad9dc406cbf9ee70833cfe33730878051270bb","observation_id":"00d21579-44e2-4487-9a9a-508287f0abb4","resolution":{"observed_at":"2026-08-11T13:08:08.088647Z","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":"2026-08-11T13:08:07.326844Z","title":"Deepsdf: Learning continuous signed distance functions for shape representation","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.326844Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:3d172a4ec8594a487d45fd8817031ef33a1dbfed5731bf0de546f8eef8feb57d","observation_id":"8d971406-8bdf-4223-bc9c-7877a6ae8dae","resolution":{"observed_at":"2026-08-11T13:08:07.326844Z","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":"2026-08-11T13:08:07.328940Z","title":"Implicit neural representations with periodic activation functions","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.328940Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:36d2b9613201d1609e78e34b2bd04f48ec0a92e861dbb230edc938a0289ecf58","observation_id":"f60864c0-a6a5-4147-9927-b6d64fd7b9d0","resolution":{"observed_at":"2026-08-11T13:08:07.328940Z","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":"2026-08-11T13:08:07.330885Z","title":"Neural fields in visual computing and beyond","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.330885Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:a47b14c876937ef138d01813a4b16ed49c3897c2ff1cc395ce77c0410750d410","observation_id":"f5955df0-25f4-4c93-8899-d61de2e93c35","resolution":{"observed_at":"2026-08-11T13:08:07.330885Z","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":"2026-08-11T13:08:07.333675Z","title":"Occupancy networks: Learning 3d reconstruction in function space","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.333675Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:d8bfb0f612be1ef5686b114ad540b6d368ea34a111f662a0ec14be7db9b8a58c","observation_id":"eedd3fe7-8663-40c1-99fa-1831f3865941","resolution":{"observed_at":"2026-08-11T13:08:07.333675Z","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":"2026-08-11T13:08:08.062748Z","title":"Points2surf learning implicit surfaces from point clouds","venue":null,"work_id":"8e541f7a-538b-481f-9602-6e9630eb6de5","year":2020},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.336968Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:530cfcab5926a6df917f21b863b88ed514d700eca66f0a586c070e5d5de9eb58","observation_id":"3357bef1-bd60-4482-87ae-3a2b08d1ba52","resolution":{"observed_at":"2026-08-11T13:08:08.064958Z","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":"2026-08-11T13:08:07.339956Z","title":"Learning implicit fields for generative shape modeling","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.339956Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:6b750403a2294f6b4bd66026719a7a8b2b56fd693feb37ac80308c48ced83a66","observation_id":"de5fdb06-bbe4-4a5c-96be-a412ba6061f0","resolution":{"observed_at":"2026-08-11T13:08:07.339956Z","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":"2026-08-11T13:08:07.342483Z","title":"Convolutional occupancy networks","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.342483Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:8aad6af6e5db62785ed7e9cc7793d46ab9e42724c21186f1d06dd765b5672249","observation_id":"3a776063-c5a6-48a5-acc6-39c978124bf4","resolution":{"observed_at":"2026-08-11T13:08:07.342483Z","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":"2026-08-11T13:08:08.049478Z","title":"Implicit functions in feature space for 3d shape reconstruction and completion","venue":null,"work_id":"67ef25ad-94f8-468f-a976-3e09b89712c9","year":2020},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.345147Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:e539d474c41e0a2df3072dcf1f707b4dd32089b5884e40eb1bea96bfef6a0b18","observation_id":"48f12bdb-cc2a-4059-8f4f-bfac9bef7ba0","resolution":{"observed_at":"2026-08-11T13:08:08.051723Z","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":"2026-08-11T13:08:08.042886Z","title":"MetaSDF: Meta- learning signed distance functions","venue":null,"work_id":"ee1c3950-0bcf-4ff7-a7d9-20c6659508d5","year":2020},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.347744Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:a415054c4145e87e2c26f8aee208041a5639b50cc675a4f5087b81e3681c3e71","observation_id":"96d69cbc-9922-498d-afd7-cb11e4ea5dfa","resolution":{"observed_at":"2026-08-11T13:08:08.045152Z","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":"2026-08-11T13:08:08.036595Z","title":"Hyperdiffusion: Generating im- plicit neural fields with weight-space diffusion","venue":null,"work_id":"72c4c2d3-c122-497a-8a1f-9bc702fdf7c2","year":2023},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.350307Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:fdc3d0162906f1d0cae94ed42069ce4dbbf13e84fd67976dd096a23900a678ab","observation_id":"2a8a8fcf-5da1-427d-8661-14b660f0d177","resolution":{"observed_at":"2026-08-11T13:08:08.038841Z","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":"2026-08-11T13:08:08.030137Z","title":"Deep learning on 3D neural fields","venue":null,"work_id":"3126c667-cb46-40ab-aefd-5b4d70f84b77","year":2023},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.353493Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:cfdc3a2c0265d1d2ab63dd1d04d51f59926f034a92388dbdd29c15393b50caa9","observation_id":"614a3039-423d-4994-946d-054f9a638abb","resolution":{"observed_at":"2026-08-11T13:08:08.032182Z","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":"2026-08-11T13:08:08.023626Z","title":"Vector neurons: A general framework for so (3)-equivariant networks","venue":null,"work_id":"3659ab3f-7504-4750-ab0c-86fa95e9d65b","year":2021},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.356605Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:5e6e1d75d4c1d55d5c6a98a7aa39dfdf8c3db83f2f1c2861b2cf120ae2f10f37","observation_id":"4a0f3ce5-1abd-4009-9e20-0921526acbac","resolution":{"observed_at":"2026-08-11T13:08:08.026177Z","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":"2026-08-11T13:08:08.016465Z","title":"Learning so (3) equivariant representations with spherical cnns","venue":null,"work_id":"8410ec08-a70b-4b5c-986c-7599049bfa4c","year":2018},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.358576Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:a82aa1ff3d2fe75186dc2b8577705b6980c9eeaf6cc64e1c7698bed01ec9a84b","observation_id":"c501e348-52e4-4821-bc87-aa969481535d","resolution":{"observed_at":"2026-08-11T13:08:08.019394Z","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":"1801.10130","last_updated":"2018-02-25T13:43:49Z","snapshot_observed_at":"2026-08-14T19:50:35.166135Z","submitted_at":"2018-01-30T18:28:30Z","title":"Spherical CNNs","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.10130","snapshot_observed_at":"2026-08-11T13:08:07.360740Z","title":"Spherical cnns","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.360740Z"},"links":{"cited_paper":"/paper/1801.10130","citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:4fbc5d3edd898a910b6b12f5fde5e96ec0761a9726db5b5ebaf3f0de9955f9bf","observation_id":"f6724272-88b6-417d-aebd-982284292bb3","resolution":{"observed_at":"2026-08-11T13:08:07.360740Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1802.08219","last_updated":"2018-05-18T20:09:34Z","snapshot_observed_at":"2026-08-18T04:32:47.717269Z","submitted_at":"2018-02-22T18:17:31Z","title":"Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.08219","snapshot_observed_at":"2026-08-11T13:08:07.363376Z","title":"Tensor field networks: Rotation-and translation-equivariant neural networks for 3D point clouds","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.363376Z"},"links":{"cited_paper":"/paper/1802.08219","citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:5ff3fc413abf1afe5a29eb7ccbc438a9dc6bab3c02a24b4cb5cdce243f1c9671","observation_id":"9c3a35cc-6247-4771-846e-ce9b83f70333","resolution":{"observed_at":"2026-08-11T13:08:07.363376Z","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":"2026-08-11T13:08:08.009344Z","title":"A functional approach to rotation equivariant non-linearities for tensor field networks","venue":null,"work_id":"2e01b1ee-5f82-471a-92d3-39617b90812c","year":2021},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.365540Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:fb84ed943c9d2fca5758435219d1cdf47e103a6979fdde7257bfce5d27bf6237","observation_id":"cf685faa-05f2-4f0a-9aca-2a318c5f6034","resolution":{"observed_at":"2026-08-11T13:08:08.012030Z","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":"1512.03012","last_updated":"2015-12-09T19:42:48Z","snapshot_observed_at":"2026-08-15T22:26:43.274625Z","submitted_at":"2015-12-09T19:42:48Z","title":"ShapeNet: An Information-Rich 3D Model Repository","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1512.03012","snapshot_observed_at":"2026-08-11T13:08:07.367202Z","title":"Shapenet: An information-rich 3d model repository","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.367202Z"},"links":{"cited_paper":"/paper/1512.03012","citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:0277c9bc043100a1d4d41536d542cdb7b4c98115f14f1663a80bc7b0fec085a0","observation_id":"fe94b556-b3eb-4b53-a0ca-fd9849567272","resolution":{"observed_at":"2026-08-11T13:08:07.367202Z","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":"2026-08-11T13:08:07.369286Z","title":"3d shapenets: A deep representation for volumetric shapes","venue":null,"work_id":null,"year":1912},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.369286Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:f5274b2a8e041025a45743c270da7c9ef2df65475cc55cfaebe8960a11a01f4d","observation_id":"95020be5-0678-43c2-8196-c4ca3e191d83","resolution":{"observed_at":"2026-08-11T13:08:07.369286Z","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":"2026-08-11T13:08:07.996698Z","title":"Learning-based point cloud registration for 6d object pose estimation in the real world","venue":null,"work_id":"edea1b93-a3d1-4d35-a07d-47865cb98433","year":2022},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.371345Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:44a109956e8c41dc18932757083bdda60b841210005a9293c66903587a2d2830","observation_id":"002ef6ab-41be-44ac-956d-47e8d44fc1a6","resolution":{"observed_at":"2026-08-11T13:08:08.000062Z","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":"2026-08-11T13:08:07.989774Z","title":"Escape from cells: Deep kd-networks for the recognition of 3d point cloud models","venue":null,"work_id":"f79c4bce-d308-4a09-b507-18cf799f41c6","year":2017},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.373083Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:9bdec101dddc47043d6e52e070a0530ab16bcf7f943fbb2432136b6528742daa","observation_id":"ab065138-a704-44ee-91b7-a927780e702d","resolution":{"observed_at":"2026-08-11T13:08:07.992163Z","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":"2026-08-11T13:08:07.982500Z","title":"PointNet++: Deep hierarchical feature learning on point sets in a metric space","venue":null,"work_id":"ed509b3a-3fa2-4ffb-864b-5b431e7096de","year":2017},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.375817Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:294180ae17ae0b75c1ddac02fe07bdfdd37f8b39f04d9d6b34d9c2bb7892075e","observation_id":"a8f74ad9-bade-429c-b91e-c40af8d4b537","resolution":{"observed_at":"2026-08-11T13:08:07.985846Z","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":"2026-08-11T13:08:07.975353Z","title":"Graph attention convolution for point cloud semantic segmentation","venue":null,"work_id":"359cde75-013f-4df9-9b72-5877686137f7","year":2019},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.378182Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:a007b5f58d80978ac8288c26f528cfd3c8b19e0afee629553f9813b08feceacf","observation_id":"cc7219c3-c959-4bec-b9f5-c4fd3dd99f79","resolution":{"observed_at":"2026-08-11T13:08:07.977987Z","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":"2026-08-11T13:08:07.968654Z","title":"Pointconv: Deep convolutional networks on 3d point clouds","venue":null,"work_id":"4f86fbf3-1ae4-4b83-9686-e5ab6967d602","year":2019},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.380072Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:31c3872887ac4fb995b4f37e4fa453adb4cd865003d1788f05ebe93ae1f8b691","observation_id":"48bae2a5-2dc5-43b2-88bd-17490d88d9af","resolution":{"observed_at":"2026-08-11T13:08:07.971013Z","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":"2026-08-11T13:08:07.962285Z","title":"Qi, Jean-Emmanuel Deschaud, Beatriz Marcotegui, François Goulette, and Leonidas J","venue":null,"work_id":"f787bee9-142e-4f4d-adc8-7edd98aa1244","year":2019},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.381821Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:fcbc2904a92f2b2f4345414b6c20b980ab43410be69a0ef20ba5bdbfa6593902","observation_id":"25e8626d-0b8f-439f-a920-fafbfbb0dbf9","resolution":{"observed_at":"2026-08-11T13:08:07.964816Z","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":"2026-08-11T13:08:07.383708Z","title":"Point transformer","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.383708Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:8df01a2a071279f77dddc6eb77358ab37710aeca6fc61ab5688e0043be11d2a7","observation_id":"4ea31227-90b7-401a-b0c4-cce7f2a8b398","resolution":{"observed_at":"2026-08-11T13:08:07.383708Z","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":"2026-08-11T13:08:07.950843Z","title":"PCT: Point cloud transformer","venue":null,"work_id":"079c0dfc-9022-47d3-8137-06f7238c87ec","year":2021},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.385570Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:6a4b43e9c0947d61969fb105591de6d5d9324c24c0cd9754405e833c82cf807f","observation_id":"7f570f2b-005b-48a2-8528-ebd838ae103b","resolution":{"observed_at":"2026-08-11T13:08:07.953134Z","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":"2026-08-11T13:08:07.943579Z","title":"Point transformer V2: Grouped vector attention and partition-based pooling","venue":null,"work_id":"515f18a9-ee6e-4cda-a52d-7570866f049d","year":2022},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.387988Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:bb4fd3dcfbd511559f37dcca9237e7e16783b4c177b870f66d2dcdb4e551611d","observation_id":"7fd7a118-c790-466d-8767-eaf6fa0d7eba","resolution":{"observed_at":"2026-08-11T13:08:07.946091Z","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":"2026-08-11T13:08:07.389799Z","title":"V oxnet: A 3d convolutional neural network for real-time object recognition","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.389799Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:43bf4ccedccebcbe05f17f5c5d19e1f3a1c66de14dc7ed061a7ecbf1ea48c130","observation_id":"eee1e289-1a25-4ef3-b9e2-6a97d3eba440","resolution":{"observed_at":"2026-08-11T13:08:07.389799Z","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":"2026-08-11T13:08:07.931774Z","title":"Octnet: Learning deep 3d representations at high resolutions","venue":null,"work_id":"c70cd4dd-a1e7-4409-9028-2fb1a7c40f3c","year":2017},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.391832Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:02fdcb73c3960bd5b4f4b42910cb93fa0210493816d83df270c01c73f4745c23","observation_id":"44ebda71-5328-4b51-9bda-a48cd4e02454","resolution":{"observed_at":"2026-08-11T13:08:07.934069Z","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":"2026-08-11T13:08:07.925701Z","title":"3d semantic segmentation with submanifold sparse convolutional networks","venue":null,"work_id":"c905e339-9044-4495-8bd9-e54c6752c8c0","year":2018},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.393742Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:d183d464730849f4748ae513dd49e03d7e046c413a680fde845a793ef8002c9c","observation_id":"3c18e127-a19f-431e-948d-a5aa09e1dfb1","resolution":{"observed_at":"2026-08-11T13:08:07.927815Z","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":"2026-08-11T13:08:07.918721Z","title":"Hodgenet: Learning spectral geometry on triangle meshes","venue":null,"work_id":"aa62cf27-5e35-400b-9a81-3e1ee5d81670","year":2021},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.395637Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:b6337bd7eed60aed43b4584149261abdfc747bbe517d90facb5c2b157b5114b2","observation_id":"9d36a823-72eb-4786-bca2-326b85c3d82c","resolution":{"observed_at":"2026-08-11T13:08:07.920838Z","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":"2026-08-11T13:08:07.911495Z","title":"Mesh convolution with continuous filters for 3-d surface parsing","venue":null,"work_id":"e9fe3449-4ab6-468e-a8fe-39d9625d0e7e","year":2023},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.397500Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:fd16feaf949ba109ccb47ecca010f994e58c684ec2afc47b469a0dcf16975200","observation_id":"a6668186-0290-4074-b0dc-8f14b2aa3966","resolution":{"observed_at":"2026-08-11T13:08:07.914611Z","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":"1809.10790","last_updated":"2018-09-27T22:45:53Z","snapshot_observed_at":"2026-08-16T18:08:09.711004Z","submitted_at":"2018-09-27T22:45:53Z","title":"Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1809.10790","snapshot_observed_at":"2026-08-11T13:08:07.399299Z","title":"Deep object pose estimation for semantic robotic grasping of household objects.arXiv preprint arXiv:1809.10790, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.399299Z"},"links":{"cited_paper":"/paper/1809.10790","citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:105b22deeb3135255d1783b481a40082458aa8f69a46ac6695aa10abae85581f","observation_id":"f858b65a-a69e-4afd-aafe-f4eedeb6f728","resolution":{"observed_at":"2026-08-11T13:08:07.399299Z","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":"2026-08-11T13:08:07.903869Z","title":"Pose estimation for augmented reality: a hands-on survey","venue":null,"work_id":"f9435ed3-5ac6-406c-a956-3078ace2b527","year":2015},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.401405Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:c66743b1e186795a4f2f2bda1d889a3ff9b65e61b41ea026c8cea8e3729e1274","observation_id":"b9cd8d6d-f9b6-4ffb-b836-fa0fc0f97993","resolution":{"observed_at":"2026-08-11T13:08:07.906845Z","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":"2026-08-11T13:08:07.403256Z","title":"Are we ready for autonomous driving? the kitti vision benchmark suite","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.403256Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:d36e6a52ee7e94193655c133a39f6941842f160c77784c0534b45997a63b14ca","observation_id":"a5bfa230-5dc5-460b-96aa-4da6c69c068c","resolution":{"observed_at":"2026-08-11T13:08:07.403256Z","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":"2026-08-11T13:08:07.799084Z","title":"Densefusion: 6d object pose estimation by iterative dense fusion","venue":null,"work_id":"821230f4-f6a7-4458-b4cb-bf997d11abe9","year":2019},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.404980Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:77daaae0e964e99a94cac540c779713cdd36e99a92b9ae9687ba4880cc184c4d","observation_id":"6ffe102d-bbf4-4f18-8bb7-6463b267ca8c","resolution":{"observed_at":"2026-08-11T13:08:07.801328Z","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":"2026-08-11T13:08:07.792615Z","title":"Normal- ized object coordinate space for category-level 6d object pose and size estimation","venue":null,"work_id":"65fdb34a-1229-43f4-ba32-ff48a8b92ed9","year":2019},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.406860Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:2780e2794bc29f188016394b8375a1e60dc3473f96276749a82169897f43d8fe","observation_id":"980e65df-ef7a-49c4-a78b-4a730f16239e","resolution":{"observed_at":"2026-08-11T13:08:07.795540Z","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":"2026-08-11T13:08:07.785285Z","title":"PVnet: Pixel-wise voting network for 6dof pose estimation","venue":null,"work_id":"86664db9-64b8-45f4-a26f-b685f1167847","year":2019},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.409152Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:fefc90e048d2940723b1269a7209fdfc1d93d028b0676d14c956fb00520c33f0","observation_id":"7379bcf6-b762-4455-9a0a-805c9756b64d","resolution":{"observed_at":"2026-08-11T13:08:07.788354Z","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":"2026-08-11T13:08:07.779450Z","title":"Pix2pose: Pixel-wise coordinate regression of objects for 6d pose estimation","venue":null,"work_id":"76dc9f46-86aa-4e89-86ac-9f0c1dc07d95","year":2019},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.411128Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:10154aada11785f3a50ee0a6b9b018fe3b44b1f00bb0e18d764aee105b51f0ff","observation_id":"ad8f0c92-d6cd-480a-b189-b4b9df64fa17","resolution":{"observed_at":"2026-08-11T13:08:07.781714Z","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":"2026-08-11T13:08:07.773615Z","title":"Center-based decoupled point-cloud registration for 6d object pose estimation","venue":null,"work_id":"2920e099-7b75-400f-8598-2c566c4bcfeb","year":2023},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.413139Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:ff2d7c216b8007405deebbd2d62efe67e229323857f666cf63f25e1c608d9555","observation_id":"91dd6ad6-fead-4696-bc22-48c7aedc7d89","resolution":{"observed_at":"2026-08-11T13:08:07.775861Z","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":"2026-08-11T13:08:07.766491Z","title":"3dmatch: Learning local geometric descriptors from rgb-d reconstructions","venue":null,"work_id":"fea3cb46-fb69-45e8-a19f-b9783e66c0c3","year":2017},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.415587Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:06996b6060cdb874f03372b0a6500252b62340098a5e5eec4f45be2f7999e90f","observation_id":"080363e5-439f-4d26-a30e-0ab8b25a4ee8","resolution":{"observed_at":"2026-08-11T13:08:07.769052Z","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":"2026-08-11T13:08:07.760153Z","title":"Fully convolutional geometric features","venue":null,"work_id":"bf1bf47c-20c9-4de7-a3d9-52697e037aeb","year":2019},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.418087Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:783395b4dda140bc95aef3e84ae067c6ad1ac4aae4d83846f0395339c841f24f","observation_id":"1cc61e08-7ebb-47c7-bdca-09587e68e69c","resolution":{"observed_at":"2026-08-11T13:08:07.762480Z","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":"2026-08-11T13:08:07.753609Z","title":"Deep closest point: Learning representations for point cloud registration","venue":null,"work_id":"82233090-0484-4ec3-8df6-979f370c3c69","year":2019},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.420451Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:8600f090628adbf0e19a6efff71a88aedff0381112cdfb1acfd1214564e8bdd3","observation_id":"e46e6d0b-de59-4ef7-84e9-36a29f349483","resolution":{"observed_at":"2026-08-11T13:08:07.756841Z","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":"2026-08-11T13:08:07.748183Z","title":"Predator: Registration of 3d point clouds with low overlap","venue":null,"work_id":"addaef37-b10e-4f02-b548-096fe45add49","year":2021},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.422422Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:332ecc4ddc7c1d5cde42e1d833e379c6d8580143ff7848446c535c0f8f757e4f","observation_id":"d49d8497-0d7f-4008-9e5f-1a411c6ba641","resolution":{"observed_at":"2026-08-11T13:08:07.750297Z","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":"2026-08-11T13:08:07.740594Z","title":"Buffer: Balancing accuracy, efficiency, and generalizability in point cloud registration","venue":null,"work_id":"68de2eec-def2-4d77-a259-27024b095b67","year":2023},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.425757Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:12322bb6acae624f8dc9ee72f570cee175c18f976910ffe2352ccbc692edd385","observation_id":"ce0d262e-fcea-490b-8810-c4bda4ff3636","resolution":{"observed_at":"2026-08-11T13:08:07.743276Z","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":"2026-08-11T13:08:07.731980Z","title":"Feature-metric registration: A fast semi-supervised approach for robust point cloud registration without correspondences","venue":null,"work_id":"49aa8379-12ec-4f44-ab49-1ab8b4213fed","year":2020},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.428197Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:3418ba941c1b2c5f71d7c0da30dd0a53c666dcc7dff4a57bd5f12b84a5000c6d","observation_id":"d1331848-ded5-41ce-b337-1d258aeeed4f","resolution":{"observed_at":"2026-08-11T13:08:07.735174Z","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":"2026-08-11T13:08:07.726211Z","title":"Pointnetlk: Robust & efficient point cloud registration using pointnet","venue":null,"work_id":"689c3aae-a9c6-4a46-b5b1-54d6b82cd34c","year":2019},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.430425Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:1cb764e26c6940e9a8cccaf2a9d97db1318009db85fb20b8a377ec41330a4d54","observation_id":"fd0f8f98-020b-4102-bede-f13be9176e0f","resolution":{"observed_at":"2026-08-11T13:08:07.728356Z","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":"2026-08-11T13:08:07.717624Z","title":"Method for registration of 3-d shapes","venue":null,"work_id":"84d03d08-20af-4351-b804-e50c953f5e8a","year":1992},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.433199Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:7971b74efc4c8de23fafaa6651aabe114e600497144633a8cd232a6d597d8409","observation_id":"c9732fa7-deb0-4056-9c36-85aba6675189","resolution":{"observed_at":"2026-08-11T13:08:07.720257Z","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":"2026-08-11T13:08:07.710468Z","title":"Fast global registration","venue":null,"work_id":"fbdfb011-5112-47e1-9372-ba8b923158b6","year":2016},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.435898Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:4531274d3560a45269515325a1f4e1075a1c393e777b174644564caa2f42db30","observation_id":"13e979d0-55ce-4db3-8069-67851045eaff","resolution":{"observed_at":"2026-08-11T13:08:07.712707Z","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":"2026-08-11T13:08:07.704249Z","title":"Teaser: Fast and certifiable point cloud registration","venue":null,"work_id":"7733b0c5-e8c8-4647-8477-64ae592ec713","year":2020},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.438383Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:fb22a6b2b40ae44fb88cf0a52314daedb91051d954916411bed2342e932191d0","observation_id":"85c5a932-04cd-4eb9-8fe8-4e3d304be6da","resolution":{"observed_at":"2026-08-11T13:08:07.706597Z","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":"2026-08-11T13:08:07.696745Z","title":"Go-icp: A globally optimal solution to 3d icp point-set registration","venue":null,"work_id":"d0dc856f-9f97-4e59-ad10-9f95355e4741","year":2015},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.440398Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:9c795184a882c49c5b071fb3d8088943ee0c9500ac32b2063954cddeacb9defb","observation_id":"cb4c4c34-2cc4-4bd5-97c7-c6fef44afd19","resolution":{"observed_at":"2026-08-11T13:08:07.699398Z","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":"2026-08-11T13:08:07.689146Z","title":"SAL: Sign agnostic learning of shapes from raw data","venue":null,"work_id":"99d4d2b4-b532-49fc-9b74-8b6984a0ad7b","year":2020},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.442275Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:c00ee8218a6156529547110fef3d9b1c023b3f0f157cd576bbdd3b8f7ebc1ece","observation_id":"023ea954-efb9-4f9f-a34f-f4ffcdd67cc9","resolution":{"observed_at":"2026-08-11T13:08:07.691330Z","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":"2026-08-11T13:08:07.682495Z","title":"Implicit geometric regularization for learning shapes","venue":null,"work_id":"83204fd3-2729-4fcd-a1d0-1e5ec88f99e9","year":2020},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.444110Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:9b2028ba121842f615a993fd6e8899563ebcba4320f75b2b7c9f65badf1e3e5b","observation_id":"3e3953ab-9fd1-448b-b761-c76da427010c","resolution":{"observed_at":"2026-08-11T13:08:07.684781Z","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":"2026-08-11T13:08:07.675831Z","title":"Nerf in the wild: Neural radiance fields for unconstrained photo collections","venue":null,"work_id":"814b7e46-05c8-483f-b3d3-001ffa3cffa5","year":2021},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.446023Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:4e1d7b87d812fd0ed4049decf5d46841b6f55cafb3aae31acda90ba9b0236b10","observation_id":"a01e0c06-1aec-4865-a3dd-157c66318ed2","resolution":{"observed_at":"2026-08-11T13:08:07.678365Z","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":"2026-08-11T13:08:07.668439Z","title":"Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction","venue":null,"work_id":"0fe29d4d-91cb-4566-ab00-e83c2f1def13","year":2021},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.447764Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:c2bafcf5dbdf21692e1f496dc1099188c8afb8039aeecf44f2cb8bec39bd4b53","observation_id":"3bf9a049-1604-460a-9ddb-50cb78dcc2b7","resolution":{"observed_at":"2026-08-11T13:08:07.670791Z","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":"2026-08-11T13:08:07.661612Z","title":"MonoSDF: Exploring monocular geometric cues for neural implicit surface reconstruction","venue":null,"work_id":"34b2a5a2-df7b-43fe-b8e3-0bc1ddad0bcb","year":2022},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.449876Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:bfd27188fde5a64baeb96e3e4b9e65e34a9d8b571e88df2f421864223a1b3662","observation_id":"187b396f-8dbc-460b-a9bc-6d8f368c8480","resolution":{"observed_at":"2026-08-11T13:08:07.664525Z","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":"2026-08-11T13:08:07.654570Z","title":"V olume rendering of neural implicit surfaces","venue":null,"work_id":"9b53f407-714a-4509-85fc-758f59865d45","year":2021},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.451879Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:0b7f9ee07dfddb50029c442bbe2aeddff746549e9710f1076565d7cc34ae35ce","observation_id":"df6f9d75-bf26-4a0c-ba53-b1265f2e5636","resolution":{"observed_at":"2026-08-11T13:08:07.657504Z","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":"2026-08-11T13:08:07.647544Z","title":"Digs: Divergence guided shape implicit neural representation for unoriented point clouds","venue":null,"work_id":"66abd7d3-932d-46ef-8e7e-d15efcad6d55","year":2022},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.453638Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:3b583613cb5252cd286abf0a01a776b10511afe04f56c7a49ca05cb389b24cf7","observation_id":"888ed19a-d0b7-45b3-bee7-d016a334e97d","resolution":{"observed_at":"2026-08-11T13:08:07.650124Z","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":"2201.12204","last_updated":"2022-11-10T13:32:44Z","snapshot_observed_at":"2026-08-16T17:25:12.938292Z","submitted_at":"2022-01-28T15:59:58Z","title":"From data to functa: Your data point is a function and you can treat it like one","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.12204","snapshot_observed_at":"2026-08-11T13:08:07.455912Z","title":"From data to functa: Your data point is a function and you can treat it like one","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.455912Z"},"links":{"cited_paper":"/paper/2201.12204","citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:d5f0df2b07cb07b85bd07442f6fd842df9efcbc63bf8d4a92968607bea3ce1b3","observation_id":"3e5dfe48-fdee-4691-9d6e-23d1ce306c50","resolution":{"observed_at":"2026-08-11T13:08:07.455912Z","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":"2026-08-11T13:08:07.639992Z","title":"Modulated periodic activations for generalizable local functional representations","venue":null,"work_id":"3ffe1c87-d3e8-43bd-be9c-f3a2af6988b9","year":2021},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.458374Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:342672195e4c8e332362d93d8c338c38b237bc1a902672be05903666d22bbb3f","observation_id":"0df38ce4-f8f1-4034-92bf-c77f90345d2b","resolution":{"observed_at":"2026-08-11T13:08:07.642876Z","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":"2026-08-11T13:08:07.632097Z","title":"pi-gan: Periodic implicit generative adversarial networks for 3d-aware image synthesis","venue":null,"work_id":"a9a0b98f-732c-4d82-8931-7e9a7e2b21cb","year":2021},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.460229Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:286b7c8d74cb71b64d74fda2007bd7071bb7feaa53a5301ca6203909e6e54f86","observation_id":"fede554b-188f-498f-9ee9-c6e9fff50a83","resolution":{"observed_at":"2026-08-11T13:08:07.634505Z","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":"2026-08-11T13:08:07.462129Z","title":"Model-agnostic meta-learning for fast adaptation of deep networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.462129Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:0b71e2afaf5f22ff3f8418bcaad56c6c420d2410f3f79f88a242ee4edd58d5e6","observation_id":"d8c855bb-e70b-4c6d-998f-09bb7ec5790e","resolution":{"observed_at":"2026-08-11T13:08:07.462129Z","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":"2026-08-11T13:08:07.618921Z","title":"Learned initializations for optimizing coordinate-based neural representations","venue":null,"work_id":"efcc7425-efa9-4333-8b59-e01ba2ea32cb","year":2021},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.464984Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:d01bfeaedfd103817dd97bfd19e56d528773d4c1a0d1cb736ddc429cb9d0f8f2","observation_id":"3e111ff0-0a6d-4b32-a267-65c411e0a14d","resolution":{"observed_at":"2026-08-11T13:08:07.621272Z","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":"2026-08-11T13:08:07.611057Z","title":"An introduction to variational autoencoders","venue":null,"work_id":"9c7e46ce-c2fc-4f30-b93c-4e39c2176237","year":2019},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.467480Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:d97431aa7819b06fe7ce826e6f076e678596d5bb2b637c9bae0d0b68b231ee43","observation_id":"7c3d0d9a-56b6-48c4-857a-418bd71fce10","resolution":{"observed_at":"2026-08-11T13:08:07.613872Z","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":"2026-08-11T13:08:07.604276Z","title":"Gensdf: Two-stage learning of generalizable signed distance functions","venue":null,"work_id":"fd7d13b6-586a-454c-be7f-70122f6e3ddd","year":2022},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.469453Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:8f2a8eab63ec15e406837be591eff38a19315fb4464d1b44c15f6442f5089e9a","observation_id":"a666f32e-64e8-4afa-a839-846d1abecbe9","resolution":{"observed_at":"2026-08-11T13:08:07.607045Z","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":"2026-08-11T13:08:07.471147Z","title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.471147Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:d5dae30c1e2353e9803e7a5791563257e98e263920ef6293963ced82446dd5e4","observation_id":"9c4a1d7e-47f5-46aa-a3d9-4f19c3b9a3d8","resolution":{"observed_at":"2026-08-11T13:08:07.471147Z","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":"2026-08-11T13:08:07.593173Z","title":"Srinivasan, Matthew Tancik, Jonathan T","venue":null,"work_id":"48a09ea3-f63d-49dd-83ee-8ae74052f7e9","year":2020},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.473611Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:b9ad1b76930c1c85d5e058a55bb97dd7b48552f0e8f0c929ef39f5daf0cb5b51","observation_id":"a6cc7380-4556-41b0-84a2-14120066a74a","resolution":{"observed_at":"2026-08-11T13:08:07.595463Z","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":"2026-08-11T13:08:07.476363Z","title":"Visualizing data using t-sne","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.476363Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:894dc5c2ac1fa160040ac0b52dc20a38c0778dd7319492d9cc2718af5cbea58e","observation_id":"9973fd2a-50ab-4573-8b60-bf094a24c6eb","resolution":{"observed_at":"2026-08-11T13:08:07.476363Z","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":"2026-08-11T13:08:07.580906Z","title":"Direct visibility of point sets","venue":null,"work_id":"fad5ba99-4197-4262-a0c3-07d96396e24e","year":2007},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.478600Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:8a8cf33d13c8a9b1212308666f31dcb9975c4fcc01861225254efd04bb27809c","observation_id":"1c1b737c-9eda-4f25-9c22-7719c2763188","resolution":{"observed_at":"2026-08-11T13:08:07.583941Z","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":"2302.03130","last_updated":"2023-02-09T12:43:24Z","snapshot_observed_at":"2026-08-16T15:57:15.936976Z","submitted_at":"2023-02-06T21:35:44Z","title":"Spatial Functa: Scaling Functa to ImageNet Classification and Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.03130","snapshot_observed_at":"2026-08-11T13:08:07.480588Z","title":"Spatial functa: Scaling functa to imagenet classification and generation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.480588Z"},"links":{"cited_paper":"/paper/2302.03130","citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:6f2ffa8b722a5afa25af7ccf501fa08467712d087a7a3222b9538a65260c31db","observation_id":"3e3f7421-1d75-44e9-980d-bcc36c601c4d","resolution":{"observed_at":"2026-08-11T13:08:07.480588Z","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":"2026-08-11T13:08:07.574234Z","title":"Generative neural fields by mixtures of neural implicit functions","venue":null,"work_id":"424281f2-77fb-4c44-a234-165ab1f0bb53","year":2024},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.483405Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:7c4d462e08cf3e9ef4f9516a5187581a678fd2d2d8ffda7c5fd1413895c8fcee","observation_id":"2cd7a999-b7ec-462c-adea-a1932f221cdc","resolution":{"observed_at":"2026-08-11T13:08:07.576799Z","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":"2026-08-11T13:08:07.485498Z","title":"Machine learning: a probabilistic perspective","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.485498Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:4b1dc9666e34c3c887e15dfb8cc35029ce1866a05e4553fb76a29b5b18e787f8","observation_id":"e255293a-b626-429c-b65a-24f5b7fe095a","resolution":{"observed_at":"2026-08-11T13:08:07.485498Z","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":"2026-08-11T13:08:07.562248Z","title":"Deep neural networks as gaussian processes","venue":null,"work_id":"8663261f-45f7-4009-84a8-f56f5764546f","year":2017},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.487695Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:55deaa36dccd45ef8c840816573c680899bbb625d9f0b714adafc970024e7c3e","observation_id":"0f466634-54b4-4e7c-b1bf-2417b56b81dd","resolution":{"observed_at":"2026-08-11T13:08:07.565637Z","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":"2026-08-11T13:08:07.489747Z","title":"Adam: A method for stochastic optimization","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.489747Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:66965e1a5c0089d4b738028528f0530289e892da5d229edcd64f209da6857308","observation_id":"3c0af478-df6b-469f-b00b-7b536d6988b4","resolution":{"observed_at":"2026-08-11T13:08:07.489747Z","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":"2026-08-11T13:08:07.553348Z","title":"Rotation and translation invariant representation learning with implicit neural representations","venue":null,"work_id":"244c286f-dbf7-48b7-addf-23be42867b36","year":2023},"citing_paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis","version":2},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-11T13:08:07.491975Z"},"links":{"citing_paper":"/paper/2412.13502"},"observation_digest":"sha256:5d6311dfe8ef1f0bf8ad96765830e6dc26f5224754e17cd981fc4fd6a7d996e6","observation_id":"cd30cfc1-190f-4ef4-9766-26a7db341fb3","resolution":{"observed_at":"2026-08-11T13:08:07.557212Z","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"}}],"paper":{"arxiv_id":"2412.13502","last_updated":"2025-07-25T05:53:01Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-13T16:59:42.635485Z","submitted_at":"2024-12-18T04:50:19Z","title":"Level-Set Parameters: Novel Representation for 3D Shape Analysis"},"reference_resolution":{"displayed":81,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":23,"verified_exact":0,"verified_fuzzy":58},"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 0 inbound Pith citation observations for arXiv:2412.13502."}