{"as_of":"2026-08-15T02:02:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7e922e928fcae4a6ada8202d8a08fa18aca6b2c6e92c5ad144674d2113e92f40","coverage":[{"denominator":37,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":37,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T12:56:08.473995Z","state":"measured"},{"denominator":37,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":37,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+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/1908.06277/citation-record","integrity":"/paper/1908.06277/integrity","json":"/paper/1908.06277/citation-record.json","paper":"/paper/1908.06277"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:56:09.323623Z","title":"Learning feed-forward one-shot learners","venue":null,"work_id":"d2bc24de-3dc4-43f2-b4f8-2a5919574e8c","year":2016},"citing_paper":{"arxiv_id":"1908.06277","last_updated":"2019-08-17T09:47:47Z","snapshot_observed_at":"2026-08-14T23:51:11.130217Z","submitted_at":"2019-08-17T09:47:47Z","title":"Deep Meta Functionals for Shape Representation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T12:56:08.216377Z"},"links":{"citing_paper":"/paper/1908.06277"},"observation_digest":"sha256:793a7a57b9320ae491b33142c3881fe733c5c75977092e5c903abf927f57c95d","observation_id":"6d4724dd-7411-4add-a335-12914298c587","resolution":{"observed_at":"2026-08-14T12:56:09.329269Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:56:09.304455Z","title":"Polygonization of implicit surfaces","venue":null,"work_id":"d626a978-c97d-40a0-9310-356b1c9f1bf9","year":1988},"citing_paper":{"arxiv_id":"1908.06277","last_updated":"2019-08-17T09:47:47Z","snapshot_observed_at":"2026-08-14T23:51:11.130217Z","submitted_at":"2019-08-17T09:47:47Z","title":"Deep Meta Functionals for Shape Representation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T12:56:08.228496Z"},"links":{"citing_paper":"/paper/1908.06277"},"observation_digest":"sha256:d18111c7cdeca643a2befcfe547c79af978ff0ebb00b4aa7e9c95429a973b6e3","observation_id":"268c3ba0-a0ef-4e60-b3bf-5173e1e772f5","resolution":{"observed_at":"2026-08-14T12:56:09.310629Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:56:09.283377Z","title":"A geometric model for active contours in image pro- cessing","venue":null,"work_id":"65ac0428-c1c1-4e74-aed9-7e511428c793","year":1993},"citing_paper":{"arxiv_id":"1908.06277","last_updated":"2019-08-17T09:47:47Z","snapshot_observed_at":"2026-08-14T23:51:11.130217Z","submitted_at":"2019-08-17T09:47:47Z","title":"Deep Meta Functionals for Shape Representation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T12:56:08.235825Z"},"links":{"citing_paper":"/paper/1908.06277"},"observation_digest":"sha256:af284ed1bf73f4415e37466f11c2a04368dfd3b3d04cbbf7ee7b8b8e116b74ff","observation_id":"73007186-ea82-47bf-9197-4306da5c8cd6","resolution":{"observed_at":"2026-08-14T12:56:09.289522Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:56:09.265671Z","title":"A geometric model for active contours in image pro- cessing","venue":null,"work_id":"f00d96d9-eba4-4aec-b914-ed4b27cbc111","year":1993},"citing_paper":{"arxiv_id":"1908.06277","last_updated":"2019-08-17T09:47:47Z","snapshot_observed_at":"2026-08-14T23:51:11.130217Z","submitted_at":"2019-08-17T09:47:47Z","title":"Deep Meta Functionals for Shape Representation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T12:56:08.241996Z"},"links":{"citing_paper":"/paper/1908.06277"},"observation_digest":"sha256:5a1ba6655e2afa3571f61c4fefd7125d654d77a9dba6f2b404200430e814d09f","observation_id":"4d5a780b-691e-431f-a469-8fc8c9b19335","resolution":{"observed_at":"2026-08-14T12:56:09.271210Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:56:09.243856Z","title":"Geodesic active contours","venue":null,"work_id":"0800af26-3fda-419f-b10a-bd405321fe1b","year":1997},"citing_paper":{"arxiv_id":"1908.06277","last_updated":"2019-08-17T09:47:47Z","snapshot_observed_at":"2026-08-14T23:51:11.130217Z","submitted_at":"2019-08-17T09:47:47Z","title":"Deep Meta Functionals for Shape Representation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T12:56:08.248658Z"},"links":{"citing_paper":"/paper/1908.06277"},"observation_digest":"sha256:b369f84bbcb8ff436f24aed393ba3fa4d8807d9ed20275c0da5c3045ec7eafe0","observation_id":"b9cc592a-68e3-4394-86bc-8a892cc45b0e","resolution":{"observed_at":"2026-08-14T12:56:09.252097Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:56:09.223022Z","title":"Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Mano- lis Savva, Shuran Song, Hao Su, Jianxiong Xiao, Li Yi, and Fisher Yu","venue":null,"work_id":"fbc32cc0-1569-4fa8-81a9-4a3b99ebf6c5","year":2015},"citing_paper":{"arxiv_id":"1908.06277","last_updated":"2019-08-17T09:47:47Z","snapshot_observed_at":"2026-08-14T23:51:11.130217Z","submitted_at":"2019-08-17T09:47:47Z","title":"Deep Meta Functionals for Shape Representation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T12:56:08.259570Z"},"links":{"citing_paper":"/paper/1908.06277"},"observation_digest":"sha256:1510bd9afc82432f2984d012b0839bd2b6c89d8b98b3c64054621fc5751eef90","observation_id":"51253e87-e3e7-4a1f-890a-dcf38e06dcc7","resolution":{"observed_at":"2026-08-14T12:56:09.228942Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:56:09.204678Z","title":"Learning implicit fields for generative shape modeling, 2018","venue":null,"work_id":"6fc4f3d3-ce62-4562-b532-bc58efb2de2f","year":2018},"citing_paper":{"arxiv_id":"1908.06277","last_updated":"2019-08-17T09:47:47Z","snapshot_observed_at":"2026-08-14T23:51:11.130217Z","submitted_at":"2019-08-17T09:47:47Z","title":"Deep Meta Functionals for Shape Representation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T12:56:08.265733Z"},"links":{"citing_paper":"/paper/1908.06277"},"observation_digest":"sha256:21431e90e5deedf1d5da33e5a2ebe03aec6a1d4fe3644426926257f8bb470bb9","observation_id":"d9e29dd6-a1dd-4ef2-91e9-d8e7b026d03f","resolution":{"observed_at":"2026-08-14T12:56:09.210587Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:56:08.271887Z","title":"3d-r2n2: A unified approach for single and multi-view 3d object reconstruction","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.06277","last_updated":"2019-08-17T09:47:47Z","snapshot_observed_at":"2026-08-14T23:51:11.130217Z","submitted_at":"2019-08-17T09:47:47Z","title":"Deep Meta Functionals for Shape Representation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T12:56:08.271887Z"},"links":{"citing_paper":"/paper/1908.06277"},"observation_digest":"sha256:0750f230d03924de2b97a1c9f097e54d3bf2e5679dccbaf431b0e7a677ff2aaf","observation_id":"36583545-8210-497a-bc77-a2413b58e4ab","resolution":{"observed_at":"2026-08-14T12:56:08.271887Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1511.07289","last_updated":"2016-02-22T07:02:58Z","snapshot_observed_at":"2026-08-14T22:21:43.095256Z","submitted_at":"2015-11-23T15:58:05Z","title":"Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.07289","snapshot_observed_at":"2026-08-14T12:56:08.283489Z","title":"Fast and accurate deep network learning by exponential linear units (elus)","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1908.06277","last_updated":"2019-08-17T09:47:47Z","snapshot_observed_at":"2026-08-14T23:51:11.130217Z","submitted_at":"2019-08-17T09:47:47Z","title":"Deep Meta Functionals for Shape Representation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T12:56:08.283489Z"},"links":{"cited_paper":"/paper/1511.07289","citing_paper":"/paper/1908.06277"},"observation_digest":"sha256:087cada41658bdfca295e8b9812ea36898d0957284b655f0ac3441a973ac8c33","observation_id":"17c87833-2887-4ac9-928e-bfab3a8ae3cf","resolution":{"observed_at":"2026-08-14T12:56:08.283489Z","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":"2026-08-14T12:56:09.174831Z","title":null,"venue":null,"work_id":"07ca914e-b955-4436-9dbc-5bac3bd1a896","year":2017},"citing_paper":{"arxiv_id":"1908.06277","last_updated":"2019-08-17T09:47:47Z","snapshot_observed_at":"2026-08-14T23:51:11.130217Z","submitted_at":"2019-08-17T09:47:47Z","title":"Deep Meta Functionals for Shape Representation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T12:56:08.291194Z"},"links":{"citing_paper":"/paper/1908.06277"},"observation_digest":"sha256:5f52eb57e18e65fd070215e30e441feb48b8e0e0d3e73b512456bf5f8072dffc","observation_id":"09d90bfc-eae3-4e69-bca9-175a6ca82352","resolution":{"observed_at":"2026-08-14T12:56:09.180398Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:56:09.156647Z","title":"Kim, Bryan C","venue":null,"work_id":"7545560c-2223-4558-a74b-1e26f7026bef","year":2018},"citing_paper":{"arxiv_id":"1908.06277","last_updated":"2019-08-17T09:47:47Z","snapshot_observed_at":"2026-08-14T23:51:11.130217Z","submitted_at":"2019-08-17T09:47:47Z","title":"Deep Meta Functionals for Shape Representation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T12:56:08.299417Z"},"links":{"citing_paper":"/paper/1908.06277"},"observation_digest":"sha256:c96782c6b6c9bc55024c680a5e37900f027f14e162097658376ae2f398785841","observation_id":"6851f20e-e4d3-4a5b-986b-66ee1cb34e29","resolution":{"observed_at":"2026-08-14T12:56:09.162578Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1609.09106","last_updated":"2016-12-01T10:08:15Z","snapshot_observed_at":"2026-08-14T14:43:47.411225Z","submitted_at":"2016-09-27T05:57:00Z","title":"HyperNetworks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.09106","snapshot_observed_at":"2026-08-14T12:56:08.321038Z","title":"Hypernetworks","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.06277","last_updated":"2019-08-17T09:47:47Z","snapshot_observed_at":"2026-08-14T23:51:11.130217Z","submitted_at":"2019-08-17T09:47:47Z","title":"Deep Meta Functionals for Shape Representation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T12:56:08.321038Z"},"links":{"cited_paper":"/paper/1609.09106","citing_paper":"/paper/1908.06277"},"observation_digest":"sha256:58e910c55668219870f7531e9d4a71c8e38fe221dab3ea6d27a2f74e5a44f877","observation_id":"90df7d0b-04d1-4187-be34-34b1768c390c","resolution":{"observed_at":"2026-08-14T12:56:08.321038Z","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":"2026-08-14T12:56:09.133582Z","title":"Hi- erarchical surface prediction for 3d object reconstruction","venue":null,"work_id":"665f13be-e121-46b4-beb6-427dda87da1d","year":2017},"citing_paper":{"arxiv_id":"1908.06277","last_updated":"2019-08-17T09:47:47Z","snapshot_observed_at":"2026-08-14T23:51:11.130217Z","submitted_at":"2019-08-17T09:47:47Z","title":"Deep Meta Functionals for Shape Representation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T12:56:08.329292Z"},"links":{"citing_paper":"/paper/1908.06277"},"observation_digest":"sha256:79d024de6eb8bbf85985aa0cd806d2ce6c9da531bc780cb9f22c6d9f606c71cd","observation_id":"eb874b6f-cd13-4ace-ae40-7d90cd98826c","resolution":{"observed_at":"2026-08-14T12:56:09.140747Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:56:09.107474Z","title":"Ray tracing algebraic surfaces","venue":null,"work_id":"7fb627a9-96bd-446c-a247-c2e064875898","year":1983},"citing_paper":{"arxiv_id":"1908.06277","last_updated":"2019-08-17T09:47:47Z","snapshot_observed_at":"2026-08-14T23:51:11.130217Z","submitted_at":"2019-08-17T09:47:47Z","title":"Deep Meta Functionals for Shape Representation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T12:56:08.335169Z"},"links":{"citing_paper":"/paper/1908.06277"},"observation_digest":"sha256:caabea8e65b62178e2f45f501992d602d7cd07a192bcd7cbc274c7445bda70ed","observation_id":"49bd0c2e-3df1-4ec5-83b5-d1f1246819b2","resolution":{"observed_at":"2026-08-14T12:56:09.114119Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:56:09.086197Z","title":"Delving deep into rectifiers: Surpassing human-level perfor- mance on imagenet classification","venue":null,"work_id":"c031d4d1-26de-4f33-914e-000630145e37","year":2015},"citing_paper":{"arxiv_id":"1908.06277","last_updated":"2019-08-17T09:47:47Z","snapshot_observed_at":"2026-08-14T23:51:11.130217Z","submitted_at":"2019-08-17T09:47:47Z","title":"Deep Meta Functionals for Shape Representation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T12:56:08.340929Z"},"links":{"citing_paper":"/paper/1908.06277"},"observation_digest":"sha256:6e9e2f495a5f6d6e34b59502f374fdcc66294a6d83a8246fc1c7857246858efd","observation_id":"db3e17d3-6df1-422d-ae2b-b887f999d9b4","resolution":{"observed_at":"2026-08-14T12:56:09.093742Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:56:09.067387Z","title":"Dynamic filter networks","venue":null,"work_id":"78443e36-e74f-4596-b1b3-cd867528ec8d","year":2016},"citing_paper":{"arxiv_id":"1908.06277","last_updated":"2019-08-17T09:47:47Z","snapshot_observed_at":"2026-08-14T23:51:11.130217Z","submitted_at":"2019-08-17T09:47:47Z","title":"Deep Meta Functionals for Shape Representation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T12:56:08.346061Z"},"links":{"citing_paper":"/paper/1908.06277"},"observation_digest":"sha256:a11a9c88d1141b9375dcb546d8371b73c11e2698a1d18ec512943b3d344e761b","observation_id":"d3630211-57c2-4895-9fac-77564dbdae5c","resolution":{"observed_at":"2026-08-14T12:56:09.072545Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:56:09.048941Z","title":"Gal: Geometric adversarial loss for single-view 3d-object recon- struction","venue":null,"work_id":"4746ad27-7c80-43dd-96fb-23c04cd60392","year":2018},"citing_paper":{"arxiv_id":"1908.06277","last_updated":"2019-08-17T09:47:47Z","snapshot_observed_at":"2026-08-14T23:51:11.130217Z","submitted_at":"2019-08-17T09:47:47Z","title":"Deep Meta Functionals for Shape Representation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T12:56:08.352288Z"},"links":{"citing_paper":"/paper/1908.06277"},"observation_digest":"sha256:d683e4864846d001e2dc32a43249875d00187d7799d2655eb07f16823b02d2e3","observation_id":"c760a188-043e-4822-a856-44cf67dce23b","resolution":{"observed_at":"2026-08-14T12:56:09.055716Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:56:09.031048Z","title":"Learning a multi-view stereo machine, 2017","venue":null,"work_id":"3afc24f7-6ddd-4b9b-bea5-795ccc7cf93b","year":2017},"citing_paper":{"arxiv_id":"1908.06277","last_updated":"2019-08-17T09:47:47Z","snapshot_observed_at":"2026-08-14T23:51:11.130217Z","submitted_at":"2019-08-17T09:47:47Z","title":"Deep Meta Functionals for Shape Representation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T12:56:08.357284Z"},"links":{"citing_paper":"/paper/1908.06277"},"observation_digest":"sha256:8a0f386d9ed9092dafde857b927ec92b6d524ed90fb0c206a996bfa8ef6d5300","observation_id":"216bfe41-ec75-421a-a2ab-8fcc9569ec37","resolution":{"observed_at":"2026-08-14T12:56:09.036305Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:56:08.362463Z","title":"Snakes: Active contour models","venue":null,"work_id":null,"year":1988},"citing_paper":{"arxiv_id":"1908.06277","last_updated":"2019-08-17T09:47:47Z","snapshot_observed_at":"2026-08-14T23:51:11.130217Z","submitted_at":"2019-08-17T09:47:47Z","title":"Deep Meta Functionals for Shape Representation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T12:56:08.362463Z"},"links":{"citing_paper":"/paper/1908.06277"},"observation_digest":"sha256:15f5774670555c90ebc8ec896150b3fba7d0be541f63143e272ed90764d9c387","observation_id":"fefc5ff8-098d-4126-9afe-1150dcf361cf","resolution":{"observed_at":"2026-08-14T12:56:08.362463Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10719","last_updated":"2019-03-29T15:18:48Z","snapshot_observed_at":"2026-08-14T17:52:59.725374Z","submitted_at":"2018-11-26T22:23:44Z","title":"Learning View Priors for Single-view 3D Reconstruction","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.10719","snapshot_observed_at":"2026-08-14T12:56:08.368415Z","title":"Learning view pri- ors for single-view 3d reconstruction","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.06277","last_updated":"2019-08-17T09:47:47Z","snapshot_observed_at":"2026-08-14T23:51:11.130217Z","submitted_at":"2019-08-17T09:47:47Z","title":"Deep Meta Functionals for Shape Representation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T12:56:08.368415Z"},"links":{"cited_paper":"/paper/1811.10719","citing_paper":"/paper/1908.06277"},"observation_digest":"sha256:031ce33e260bfdb1014ca2ea93a71826c550df23e633660a8a08c7a74eecdc82","observation_id":"1aa790b0-1437-4816-a124-971946678224","resolution":{"observed_at":"2026-08-14T12:56:08.368415Z","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":"2026-08-14T12:56:08.991905Z","title":"Neu- ral 3d mesh renderer","venue":null,"work_id":"b8381924-0202-4535-aed5-6d7a05282eca","year":2018},"citing_paper":{"arxiv_id":"1908.06277","last_updated":"2019-08-17T09:47:47Z","snapshot_observed_at":"2026-08-14T23:51:11.130217Z","submitted_at":"2019-08-17T09:47:47Z","title":"Deep Meta Functionals for Shape Representation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T12:56:08.375452Z"},"links":{"citing_paper":"/paper/1908.06277"},"observation_digest":"sha256:f964f55f69fb179ce66efa77bada64a56e100d98fd0239149d3bf9bf399a8d5e","observation_id":"e02599ad-6942-4cfd-9d2e-534751721039","resolution":{"observed_at":"2026-08-14T12:56:09.000114Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:56:08.969510Z","title":"Gradient flows and ge- ometric active contour models","venue":null,"work_id":"11e81949-b904-4b0d-99cf-b3df5bfc2b50","year":1995},"citing_paper":{"arxiv_id":"1908.06277","last_updated":"2019-08-17T09:47:47Z","snapshot_observed_at":"2026-08-14T23:51:11.130217Z","submitted_at":"2019-08-17T09:47:47Z","title":"Deep Meta Functionals for Shape Representation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T12:56:08.384996Z"},"links":{"citing_paper":"/paper/1908.06277"},"observation_digest":"sha256:3675d098bf6b330ef1ee357943e19aec94058a4fcf00e03676d9f047cc894b28","observation_id":"074eff2d-ee9d-4c8b-9061-ea9c158f5693","resolution":{"observed_at":"2026-08-14T12:56:08.976498Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:56:08.939789Z","title":"A dynamic convolutional layer for short range weather prediction","venue":null,"work_id":"9d2a1b57-52b7-4c47-97c3-a42c484f2eb6","year":2015},"citing_paper":{"arxiv_id":"1908.06277","last_updated":"2019-08-17T09:47:47Z","snapshot_observed_at":"2026-08-14T23:51:11.130217Z","submitted_at":"2019-08-17T09:47:47Z","title":"Deep Meta Functionals for Shape Representation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T12:56:08.391791Z"},"links":{"citing_paper":"/paper/1908.06277"},"observation_digest":"sha256:8e5d0190f1ac43a15042b61710c7097b871614238721d3249d794809e81ec276","observation_id":"9f4a7670-9ba3-4428-b53c-4c5b2d4795f0","resolution":{"observed_at":"2026-08-14T12:56:08.948628Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:56:08.908553Z","title":"Kosinski","venue":null,"work_id":"f9dcd580-5b5e-43ed-b2c3-045b0bc7ebee","year":2007},"citing_paper":{"arxiv_id":"1908.06277","last_updated":"2019-08-17T09:47:47Z","snapshot_observed_at":"2026-08-14T23:51:11.130217Z","submitted_at":"2019-08-17T09:47:47Z","title":"Deep Meta Functionals for Shape Representation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T12:56:08.396885Z"},"links":{"citing_paper":"/paper/1908.06277"},"observation_digest":"sha256:9f4a1ab0f2599a834fb4eba49d9ab038966d60d8d287d3d5fdf488191cbd43c1","observation_id":"adc65205-3a54-4e28-8f51-3f10fc329eaf","resolution":{"observed_at":"2026-08-14T12:56:08.920022Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:56:08.885155Z","title":"Active contour based segmentation of 3d surfaces","venue":null,"work_id":"618a5f1f-bf46-4951-bcf0-50590af33006","year":2008},"citing_paper":{"arxiv_id":"1908.06277","last_updated":"2019-08-17T09:47:47Z","snapshot_observed_at":"2026-08-14T23:51:11.130217Z","submitted_at":"2019-08-17T09:47:47Z","title":"Deep Meta Functionals for Shape Representation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T12:56:08.406576Z"},"links":{"citing_paper":"/paper/1908.06277"},"observation_digest":"sha256:159486b6df5ee35749ebb24890491840421b8daf07d74ebc10f0598b6e4af59c","observation_id":"5b68bc01-a738-4605-9e73-0a0e41053b43","resolution":{"observed_at":"2026-08-14T12:56:08.892004Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1901.05567","last_updated":"2019-01-23T00:32:24Z","snapshot_observed_at":"2026-08-14T17:29:55.644752Z","submitted_at":"2019-01-17T00:00:58Z","title":"Soft Rasterizer: Differentiable Rendering for Unsupervised Single-View Mesh Reconstruction","version":2},"cited_work":{"arxiv_id":"1901.05567","doi":null,"metadata_source":"pith","pith_arxiv_id":"1901.05567","snapshot_observed_at":"2026-08-14T12:56:08.523504Z","title":"Soft Rasterizer: Differentiable Rendering for Unsupervised Single-View Mesh Reconstruction","venue":"cs.CV","work_id":"5afff888-1b4a-40a7-95b3-bfe8506f1d61","year":2019},"citing_paper":{"arxiv_id":"1908.06277","last_updated":"2019-08-17T09:47:47Z","snapshot_observed_at":"2026-08-14T23:51:11.130217Z","submitted_at":"2019-08-17T09:47:47Z","title":"Deep Meta Functionals for Shape Representation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T12:56:08.411987Z"},"links":{"cited_paper":"/paper/1901.05567","citing_paper":"/paper/1908.06277"},"observation_digest":"sha256:09f36260715b6c806ae12d4ca8de9f6a61241074048e4e8b057bfca554156b7d","observation_id":"851211f5-9334-40b1-a90f-22f95aff3ffe","resolution":{"observed_at":"2026-08-14T12:56:08.532341Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:56:08.864728Z","title":"Lorensen and Harvey E","venue":null,"work_id":"4957d3a8-3123-4c11-b162-fbee0009604c","year":1987},"citing_paper":{"arxiv_id":"1908.06277","last_updated":"2019-08-17T09:47:47Z","snapshot_observed_at":"2026-08-14T23:51:11.130217Z","submitted_at":"2019-08-17T09:47:47Z","title":"Deep Meta Functionals for Shape Representation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T12:56:08.420044Z"},"links":{"citing_paper":"/paper/1908.06277"},"observation_digest":"sha256:c7e06a7d86a109af30e9126fbdada48bd09fb01327417edf20e2b8a3a8f0e978","observation_id":"9b2e8487-f7ba-44dc-b693-b268074f21e3","resolution":{"observed_at":"2026-08-14T12:56:08.871660Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:56:08.841746Z","title":"Evolu- tionary fronts for topology-independent shape modeling and recovery","venue":null,"work_id":"d72a82f5-28ed-49ac-93b6-21a062b9498d","year":1994},"citing_paper":{"arxiv_id":"1908.06277","last_updated":"2019-08-17T09:47:47Z","snapshot_observed_at":"2026-08-14T23:51:11.130217Z","submitted_at":"2019-08-17T09:47:47Z","title":"Deep Meta Functionals for Shape Representation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T12:56:08.425333Z"},"links":{"citing_paper":"/paper/1908.06277"},"observation_digest":"sha256:9a432305a323a46d72eca8b2a73789e08bc8f6fd0e8e53b3fcd4c30131f930b1","observation_id":"a90de87d-e647-447e-8681-59d495c7fe4a","resolution":{"observed_at":"2026-08-14T12:56:08.847940Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:56:08.804490Z","title":"Occupancy networks: Learning 3d reconstruction in function space, 2018","venue":null,"work_id":"5bb6388c-094d-4088-97ee-a3f2aef33ee3","year":2018},"citing_paper":{"arxiv_id":"1908.06277","last_updated":"2019-08-17T09:47:47Z","snapshot_observed_at":"2026-08-14T23:51:11.130217Z","submitted_at":"2019-08-17T09:47:47Z","title":"Deep Meta Functionals for Shape Representation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-14T12:56:08.430689Z"},"links":{"citing_paper":"/paper/1908.06277"},"observation_digest":"sha256:d98f82ce3b1ea9ab73daa558ff58c7cad5d402921af079f9bc0a89ff81c0803f","observation_id":"bfaf16cc-0010-4251-ac7f-62a14791fcf2","resolution":{"observed_at":"2026-08-14T12:56:08.819001Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:56:08.771005Z","title":"On the number of linear regions of deep neural networks","venue":null,"work_id":"9ecee53d-e6ed-45a3-826e-d947bd2a32d3","year":2014},"citing_paper":{"arxiv_id":"1908.06277","last_updated":"2019-08-17T09:47:47Z","snapshot_observed_at":"2026-08-14T23:51:11.130217Z","submitted_at":"2019-08-17T09:47:47Z","title":"Deep Meta Functionals for Shape Representation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-14T12:56:08.435516Z"},"links":{"citing_paper":"/paper/1908.06277"},"observation_digest":"sha256:ddb3af1d105a64f32d61f1d3698e47aa7ff0055a34d24c1faf793000bae8e390","observation_id":"697d3dcb-3bcd-496f-a033-0ef4fa1ebfde","resolution":{"observed_at":"2026-08-14T12:56:08.776738Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:56:08.440787Z","title":"Deepsdf: Learning con- tinuous signed distance functions for shape representation,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.06277","last_updated":"2019-08-17T09:47:47Z","snapshot_observed_at":"2026-08-14T23:51:11.130217Z","submitted_at":"2019-08-17T09:47:47Z","title":"Deep Meta Functionals for Shape Representation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-14T12:56:08.440787Z"},"links":{"citing_paper":"/paper/1908.06277"},"observation_digest":"sha256:b617f3b697818f5d8b62cfef9251638200f7a348d43f22e017bccb2fb78cca47","observation_id":"5ae52b79-48e6-4036-a6a3-a0e2749defa8","resolution":{"observed_at":"2026-08-14T12:56:08.440787Z","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":"2026-08-14T12:56:08.736465Z","title":"Matryoshka networks: Predicting 3d geometry via nested shape layers","venue":null,"work_id":"603d9256-dca0-4632-9664-8ee20078ad1b","year":1936},"citing_paper":{"arxiv_id":"1908.06277","last_updated":"2019-08-17T09:47:47Z","snapshot_observed_at":"2026-08-14T23:51:11.130217Z","submitted_at":"2019-08-17T09:47:47Z","title":"Deep Meta Functionals for Shape Representation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-14T12:56:08.445939Z"},"links":{"citing_paper":"/paper/1908.06277"},"observation_digest":"sha256:2a3b7bf0fe35c11ad2f28ee95134ef69578e86a703701752678c419893042229","observation_id":"f78e81e5-3c23-4aa2-9ec8-f862e098784b","resolution":{"observed_at":"2026-08-14T12:56:08.742762Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:56:08.716381Z","title":"Riegler, S","venue":null,"work_id":"99e08188-0c3f-4d07-84ee-51b4985e8053","year":2015},"citing_paper":{"arxiv_id":"1908.06277","last_updated":"2019-08-17T09:47:47Z","snapshot_observed_at":"2026-08-14T23:51:11.130217Z","submitted_at":"2019-08-17T09:47:47Z","title":"Deep Meta Functionals for Shape Representation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-14T12:56:08.452401Z"},"links":{"citing_paper":"/paper/1908.06277"},"observation_digest":"sha256:297cb9ce68023a7d8abb4443c91290a89e35066211a4c80a7af1c3b30abbb009","observation_id":"4af68026-0bd9-4a9f-97c1-3ecbd1556352","resolution":{"observed_at":"2026-08-14T12:56:08.724277Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:56:08.690361Z","title":"Octnet: Learning deep 3d representations at high resolu- tions","venue":null,"work_id":"739d9226-6cde-4a6d-a71c-40ee05a6e9a5","year":2017},"citing_paper":{"arxiv_id":"1908.06277","last_updated":"2019-08-17T09:47:47Z","snapshot_observed_at":"2026-08-14T23:51:11.130217Z","submitted_at":"2019-08-17T09:47:47Z","title":"Deep Meta Functionals for Shape Representation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-14T12:56:08.458009Z"},"links":{"citing_paper":"/paper/1908.06277"},"observation_digest":"sha256:085d4506dbdd01421905bbecc9881e5d18d0a32fb94e35de695aa3451917844c","observation_id":"c9e3d2d8-6eb6-4805-ad4d-28a90d98e1f5","resolution":{"observed_at":"2026-08-14T12:56:08.696181Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:56:08.669564Z","title":"Octree generating networks: Efficient convolutional archi- tectures for high-resolution 3d outputs","venue":null,"work_id":"95fddd2b-17dd-424e-ab17-d7cfbcbff251","year":2017},"citing_paper":{"arxiv_id":"1908.06277","last_updated":"2019-08-17T09:47:47Z","snapshot_observed_at":"2026-08-14T23:51:11.130217Z","submitted_at":"2019-08-17T09:47:47Z","title":"Deep Meta Functionals for Shape Representation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-14T12:56:08.463080Z"},"links":{"citing_paper":"/paper/1908.06277"},"observation_digest":"sha256:c2c9edd15946004276e3919ea765311ce4c17ab7015a55fdc84bb33d6eaca34b","observation_id":"05587d0e-b316-4192-8952-86a4dd4f1332","resolution":{"observed_at":"2026-08-14T12:56:08.675222Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:56:08.468205Z","title":"Pixel2mesh: Generating 3d mesh models from single rgb images","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.06277","last_updated":"2019-08-17T09:47:47Z","snapshot_observed_at":"2026-08-14T23:51:11.130217Z","submitted_at":"2019-08-17T09:47:47Z","title":"Deep Meta Functionals for Shape Representation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-14T12:56:08.468205Z"},"links":{"citing_paper":"/paper/1908.06277"},"observation_digest":"sha256:8ad9cf9095018dda06c84258eb2ad935aad2a4593a3fa8f536102d7c220a754c","observation_id":"c4a3a638-7f2d-4146-b9f3-b61f1be058ab","resolution":{"observed_at":"2026-08-14T12:56:08.468205Z","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":"2026-08-14T12:56:08.635727Z","title":"Inferring point clouds from single monocular images by depth intermedia- tion, 2018","venue":null,"work_id":"5dec9f4c-c8ed-4dd5-a7f8-68c96f19b99a","year":2018},"citing_paper":{"arxiv_id":"1908.06277","last_updated":"2019-08-17T09:47:47Z","snapshot_observed_at":"2026-08-14T23:51:11.130217Z","submitted_at":"2019-08-17T09:47:47Z","title":"Deep Meta Functionals for Shape Representation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-14T12:56:08.473995Z"},"links":{"citing_paper":"/paper/1908.06277"},"observation_digest":"sha256:0038fffe739d411e68ac304ff60a32adb483f0100fa3118950c0338b99644cf8","observation_id":"e6758725-1981-46ba-bed6-169b2b6270d5","resolution":{"observed_at":"2026-08-14T12:56:08.644071Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1908.06277","last_updated":"2019-08-17T09:47:47Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-14T23:51:11.130217Z","submitted_at":"2019-08-17T09:47:47Z","title":"Deep Meta Functionals for Shape Representation"},"reference_resolution":{"displayed":37,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":8,"verified_exact":1,"verified_fuzzy":28},"total_outbound_references":37},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:1908.06277."}