{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:UTA5MRES7XTSVRFSPVYT7OGNFV","short_pith_number":"pith:UTA5MRES","schema_version":"1.0","canonical_sha256":"a4c1d64492fde72ac4b27d713fb8cd2d76b7cfab9bba3b60985ea7c11faedc68","source":{"kind":"arxiv","id":"2405.19678","version":2},"attestation_state":"computed","paper":{"title":"View-Consistent Hierarchical 3D Segmentation Using Ultrametric Feature Fields","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Adam W. Harley, Colton Stearns, Haodi He, Leonidas J. Guibas","submitted_at":"2024-05-30T04:14:58Z","abstract_excerpt":"Large-scale vision foundation models such as Segment Anything (SAM) demonstrate impressive performance in zero-shot image segmentation at multiple levels of granularity. However, these zero-shot predictions are rarely 3D-consistent. As the camera viewpoint changes in a scene, so do the segmentation predictions, as well as the characterizations of \"coarse\" or \"fine\" granularity. In this work, we address the challenging task of lifting multi-granular and view-inconsistent image segmentations into a hierarchical and 3D-consistent representation. We learn a novel feature field within a Neural Radi"},"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":"2405.19678","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-05-30T04:14:58Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"a756cbd0c4c93b7e5c4858076c2dfe808670db3df7608168c4f6437bf17d1ab3","abstract_canon_sha256":"e806903b6f75a3926b8dd92be51533dd6d41084f4d50e38ce8201bc50d29d82e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:45:29.093019Z","signature_b64":"IUt30Rl1VCIW64yl039vzpm1JAJ98hRF4aLLWFZwQagOjDyLw/ky8F5GDGOwptLjGM8cBYaSYib2aRXxwFYGDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a4c1d64492fde72ac4b27d713fb8cd2d76b7cfab9bba3b60985ea7c11faedc68","last_reissued_at":"2026-07-05T08:45:29.092626Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:45:29.092626Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"View-Consistent Hierarchical 3D Segmentation Using Ultrametric Feature Fields","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Adam W. Harley, Colton Stearns, Haodi He, Leonidas J. Guibas","submitted_at":"2024-05-30T04:14:58Z","abstract_excerpt":"Large-scale vision foundation models such as Segment Anything (SAM) demonstrate impressive performance in zero-shot image segmentation at multiple levels of granularity. However, these zero-shot predictions are rarely 3D-consistent. As the camera viewpoint changes in a scene, so do the segmentation predictions, as well as the characterizations of \"coarse\" or \"fine\" granularity. In this work, we address the challenging task of lifting multi-granular and view-inconsistent image segmentations into a hierarchical and 3D-consistent representation. We learn a novel feature field within a Neural Radi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.19678","kind":"arxiv","version":2},"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/2405.19678/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":"2405.19678","created_at":"2026-07-05T08:45:29.092682+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.19678v2","created_at":"2026-07-05T08:45:29.092682+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.19678","created_at":"2026-07-05T08:45:29.092682+00:00"},{"alias_kind":"pith_short_12","alias_value":"UTA5MRES7XTS","created_at":"2026-07-05T08:45:29.092682+00:00"},{"alias_kind":"pith_short_16","alias_value":"UTA5MRES7XTSVRFS","created_at":"2026-07-05T08:45:29.092682+00:00"},{"alias_kind":"pith_short_8","alias_value":"UTA5MRES","created_at":"2026-07-05T08:45:29.092682+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UTA5MRES7XTSVRFSPVYT7OGNFV","json":"https://pith.science/pith/UTA5MRES7XTSVRFSPVYT7OGNFV.json","graph_json":"https://pith.science/api/pith-number/UTA5MRES7XTSVRFSPVYT7OGNFV/graph.json","events_json":"https://pith.science/api/pith-number/UTA5MRES7XTSVRFSPVYT7OGNFV/events.json","paper":"https://pith.science/paper/UTA5MRES"},"agent_actions":{"view_html":"https://pith.science/pith/UTA5MRES7XTSVRFSPVYT7OGNFV","download_json":"https://pith.science/pith/UTA5MRES7XTSVRFSPVYT7OGNFV.json","view_paper":"https://pith.science/paper/UTA5MRES","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.19678&json=true","fetch_graph":"https://pith.science/api/pith-number/UTA5MRES7XTSVRFSPVYT7OGNFV/graph.json","fetch_events":"https://pith.science/api/pith-number/UTA5MRES7XTSVRFSPVYT7OGNFV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UTA5MRES7XTSVRFSPVYT7OGNFV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UTA5MRES7XTSVRFSPVYT7OGNFV/action/storage_attestation","attest_author":"https://pith.science/pith/UTA5MRES7XTSVRFSPVYT7OGNFV/action/author_attestation","sign_citation":"https://pith.science/pith/UTA5MRES7XTSVRFSPVYT7OGNFV/action/citation_signature","submit_replication":"https://pith.science/pith/UTA5MRES7XTSVRFSPVYT7OGNFV/action/replication_record"}},"created_at":"2026-07-05T08:45:29.092682+00:00","updated_at":"2026-07-05T08:45:29.092682+00:00"}