{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:VQGXF2I7SSLVQM5Q36UUSHZ4CE","short_pith_number":"pith:VQGXF2I7","schema_version":"1.0","canonical_sha256":"ac0d72e91f94975833b0dfa9491f3c111fee7c98493aa0c93ac9e29145e1994f","source":{"kind":"arxiv","id":"2004.03967","version":1},"attestation_state":"computed","paper":{"title":"Learning 3D Semantic Scene Graphs from 3D Indoor Reconstructions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Federico Tombari, Helisa Dhamo, Johanna Wald, Nassir Navab","submitted_at":"2020-04-08T12:25:25Z","abstract_excerpt":"Scene understanding has been of high interest in computer vision. It encompasses not only identifying objects in a scene, but also their relationships within the given context. With this goal, a recent line of works tackles 3D semantic segmentation and scene layout prediction. In our work we focus on scene graphs, a data structure that organizes the entities of a scene in a graph, where objects are nodes and their relationships modeled as edges. We leverage inference on scene graphs as a way to carry out 3D scene understanding, mapping objects and their relationships. In particular, we propose"},"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":"2004.03967","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-04-08T12:25:25Z","cross_cats_sorted":[],"title_canon_sha256":"357a546c230cd79d857f528b5e43f15422b86d1d8cefd7e8520904cc5ca34e77","abstract_canon_sha256":"46702e11230f83c1824eb17d96d4901281bbc13c190ba8101fe1188b92eb0dcd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:53:57.988525Z","signature_b64":"pKfj6B6Q9yXmrarQ5IoY30/fv1756gPSGgjSZtgPnmbCemSXuA7tXHM4CZO+I05dT+YIyGcfFp80yQCaoPVbAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ac0d72e91f94975833b0dfa9491f3c111fee7c98493aa0c93ac9e29145e1994f","last_reissued_at":"2026-07-05T00:53:57.988055Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:53:57.988055Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning 3D Semantic Scene Graphs from 3D Indoor Reconstructions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Federico Tombari, Helisa Dhamo, Johanna Wald, Nassir Navab","submitted_at":"2020-04-08T12:25:25Z","abstract_excerpt":"Scene understanding has been of high interest in computer vision. It encompasses not only identifying objects in a scene, but also their relationships within the given context. With this goal, a recent line of works tackles 3D semantic segmentation and scene layout prediction. In our work we focus on scene graphs, a data structure that organizes the entities of a scene in a graph, where objects are nodes and their relationships modeled as edges. We leverage inference on scene graphs as a way to carry out 3D scene understanding, mapping objects and their relationships. In particular, we propose"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2004.03967","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/2004.03967/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":"2004.03967","created_at":"2026-07-05T00:53:57.988109+00:00"},{"alias_kind":"arxiv_version","alias_value":"2004.03967v1","created_at":"2026-07-05T00:53:57.988109+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2004.03967","created_at":"2026-07-05T00:53:57.988109+00:00"},{"alias_kind":"pith_short_12","alias_value":"VQGXF2I7SSLV","created_at":"2026-07-05T00:53:57.988109+00:00"},{"alias_kind":"pith_short_16","alias_value":"VQGXF2I7SSLVQM5Q","created_at":"2026-07-05T00:53:57.988109+00:00"},{"alias_kind":"pith_short_8","alias_value":"VQGXF2I7","created_at":"2026-07-05T00:53:57.988109+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.21788","citing_title":"SceneGraphGrounder: Zero-Shot 3D Visual Grounding via Structured Scene Graph Matching","ref_index":17,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VQGXF2I7SSLVQM5Q36UUSHZ4CE","json":"https://pith.science/pith/VQGXF2I7SSLVQM5Q36UUSHZ4CE.json","graph_json":"https://pith.science/api/pith-number/VQGXF2I7SSLVQM5Q36UUSHZ4CE/graph.json","events_json":"https://pith.science/api/pith-number/VQGXF2I7SSLVQM5Q36UUSHZ4CE/events.json","paper":"https://pith.science/paper/VQGXF2I7"},"agent_actions":{"view_html":"https://pith.science/pith/VQGXF2I7SSLVQM5Q36UUSHZ4CE","download_json":"https://pith.science/pith/VQGXF2I7SSLVQM5Q36UUSHZ4CE.json","view_paper":"https://pith.science/paper/VQGXF2I7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2004.03967&json=true","fetch_graph":"https://pith.science/api/pith-number/VQGXF2I7SSLVQM5Q36UUSHZ4CE/graph.json","fetch_events":"https://pith.science/api/pith-number/VQGXF2I7SSLVQM5Q36UUSHZ4CE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VQGXF2I7SSLVQM5Q36UUSHZ4CE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VQGXF2I7SSLVQM5Q36UUSHZ4CE/action/storage_attestation","attest_author":"https://pith.science/pith/VQGXF2I7SSLVQM5Q36UUSHZ4CE/action/author_attestation","sign_citation":"https://pith.science/pith/VQGXF2I7SSLVQM5Q36UUSHZ4CE/action/citation_signature","submit_replication":"https://pith.science/pith/VQGXF2I7SSLVQM5Q36UUSHZ4CE/action/replication_record"}},"created_at":"2026-07-05T00:53:57.988109+00:00","updated_at":"2026-07-05T00:53:57.988109+00:00"}