{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:IGJ7ABE54X3Q7TSBYISSGLDRGO","short_pith_number":"pith:IGJ7ABE5","schema_version":"1.0","canonical_sha256":"4193f0049de5f70fce41c225232c71338a58e31960cce77ff56d892526fc9c4f","source":{"kind":"arxiv","id":"2304.06342","version":1},"attestation_state":"computed","paper":{"title":"RoSI: Recovering 3D Shape Interiors from Few Articulation Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"Akshay Gadi Patil, Brian Jackson, Eric Bennett, Hao Zhang, Shan Yang, Yiming Qian","submitted_at":"2023-04-13T08:45:26Z","abstract_excerpt":"The dominant majority of 3D models that appear in gaming, VR/AR, and those we use to train geometric deep learning algorithms are incomplete, since they are modeled as surface meshes and missing their interior structures. We present a learning framework to recover the shape interiors (RoSI) of existing 3D models with only their exteriors from multi-view and multi-articulation images. Given a set of RGB images that capture a target 3D object in different articulated poses, possibly from only few views, our method infers the interior planes that are observable in the input images. Our neural arc"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2304.06342","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-04-13T08:45:26Z","cross_cats_sorted":["cs.GR"],"title_canon_sha256":"babce9f1e3d63788febe04c5ddff33cc2dd349f1ba1ed50409fd94749b2a6b5e","abstract_canon_sha256":"d827caa78685f815d4214e00cd7988c455fd8ed93fe3db6eb46554aa742b5b3d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:00:42.331420Z","signature_b64":"cewR3gJRZOFAxDWipKn42b7uUicrSe9TteShGSlEyeVDW7ZZb2srStNX6XBnoBDTgsPcIWTsyGlfjbbS5NKhDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4193f0049de5f70fce41c225232c71338a58e31960cce77ff56d892526fc9c4f","last_reissued_at":"2026-07-05T06:00:42.330926Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:00:42.330926Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RoSI: Recovering 3D Shape Interiors from Few Articulation Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR"],"primary_cat":"cs.CV","authors_text":"Akshay Gadi Patil, Brian Jackson, Eric Bennett, Hao Zhang, Shan Yang, Yiming Qian","submitted_at":"2023-04-13T08:45:26Z","abstract_excerpt":"The dominant majority of 3D models that appear in gaming, VR/AR, and those we use to train geometric deep learning algorithms are incomplete, since they are modeled as surface meshes and missing their interior structures. We present a learning framework to recover the shape interiors (RoSI) of existing 3D models with only their exteriors from multi-view and multi-articulation images. Given a set of RGB images that capture a target 3D object in different articulated poses, possibly from only few views, our method infers the interior planes that are observable in the input images. Our neural arc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.06342","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2304.06342/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2304.06342","created_at":"2026-07-05T06:00:42.330986+00:00"},{"alias_kind":"arxiv_version","alias_value":"2304.06342v1","created_at":"2026-07-05T06:00:42.330986+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.06342","created_at":"2026-07-05T06:00:42.330986+00:00"},{"alias_kind":"pith_short_12","alias_value":"IGJ7ABE54X3Q","created_at":"2026-07-05T06:00:42.330986+00:00"},{"alias_kind":"pith_short_16","alias_value":"IGJ7ABE54X3Q7TSB","created_at":"2026-07-05T06:00:42.330986+00:00"},{"alias_kind":"pith_short_8","alias_value":"IGJ7ABE5","created_at":"2026-07-05T06:00:42.330986+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.07758","citing_title":"DailyArt: Discovering Articulation from Single Static Images via Latent Dynamics","ref_index":31,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IGJ7ABE54X3Q7TSBYISSGLDRGO","json":"https://pith.science/pith/IGJ7ABE54X3Q7TSBYISSGLDRGO.json","graph_json":"https://pith.science/api/pith-number/IGJ7ABE54X3Q7TSBYISSGLDRGO/graph.json","events_json":"https://pith.science/api/pith-number/IGJ7ABE54X3Q7TSBYISSGLDRGO/events.json","paper":"https://pith.science/paper/IGJ7ABE5"},"agent_actions":{"view_html":"https://pith.science/pith/IGJ7ABE54X3Q7TSBYISSGLDRGO","download_json":"https://pith.science/pith/IGJ7ABE54X3Q7TSBYISSGLDRGO.json","view_paper":"https://pith.science/paper/IGJ7ABE5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2304.06342&json=true","fetch_graph":"https://pith.science/api/pith-number/IGJ7ABE54X3Q7TSBYISSGLDRGO/graph.json","fetch_events":"https://pith.science/api/pith-number/IGJ7ABE54X3Q7TSBYISSGLDRGO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IGJ7ABE54X3Q7TSBYISSGLDRGO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IGJ7ABE54X3Q7TSBYISSGLDRGO/action/storage_attestation","attest_author":"https://pith.science/pith/IGJ7ABE54X3Q7TSBYISSGLDRGO/action/author_attestation","sign_citation":"https://pith.science/pith/IGJ7ABE54X3Q7TSBYISSGLDRGO/action/citation_signature","submit_replication":"https://pith.science/pith/IGJ7ABE54X3Q7TSBYISSGLDRGO/action/replication_record"}},"created_at":"2026-07-05T06:00:42.330986+00:00","updated_at":"2026-07-05T06:00:42.330986+00:00"}