{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:VRKBSS2QP5YPFLANDF2G6IUVXK","short_pith_number":"pith:VRKBSS2Q","schema_version":"1.0","canonical_sha256":"ac54194b507f70f2ac0d19746f2295babbb5f113c37fd5de44f6e48445f9fa2e","source":{"kind":"arxiv","id":"2503.06469","version":1},"attestation_state":"computed","paper":{"title":"Vector Quantized Feature Fields for Fast 3D Semantic Lifting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aditya Agarwal, George Tang, Trevor Darrell, Weiqiao Han, Yutong Bai","submitted_at":"2025-03-09T06:12:30Z","abstract_excerpt":"We generalize lifting to semantic lifting by incorporating per-view masks that indicate relevant pixels for lifting tasks. These masks are determined by querying corresponding multiscale pixel-aligned feature maps, which are derived from scene representations such as distilled feature fields and feature point clouds. However, storing per-view feature maps rendered from distilled feature fields is impractical, and feature point clouds are expensive to store and query. To enable lightweight on-demand retrieval of pixel-aligned relevance masks, we introduce the Vector-Quantized Feature Field. We "},"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":"2503.06469","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-09T06:12:30Z","cross_cats_sorted":[],"title_canon_sha256":"152293bc359a56293290741906f5354776bf828a7c869d9e3ee813fbc77de83f","abstract_canon_sha256":"15025e60d491e42ba8de5bf24a9bfa340b1c473b3ef6fcb4ac4869a58412ed73"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:27:34.505330Z","signature_b64":"AVHfkk3sUYacBf6F9pdWQTG5j6GkNamAoq2XLs23rcEEIrNfbu+Rcn+THVb0UrE/DbK6gnFY8eBIoTd/uBBtBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ac54194b507f70f2ac0d19746f2295babbb5f113c37fd5de44f6e48445f9fa2e","last_reissued_at":"2026-07-05T10:27:34.504777Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:27:34.504777Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Vector Quantized Feature Fields for Fast 3D Semantic Lifting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aditya Agarwal, George Tang, Trevor Darrell, Weiqiao Han, Yutong Bai","submitted_at":"2025-03-09T06:12:30Z","abstract_excerpt":"We generalize lifting to semantic lifting by incorporating per-view masks that indicate relevant pixels for lifting tasks. These masks are determined by querying corresponding multiscale pixel-aligned feature maps, which are derived from scene representations such as distilled feature fields and feature point clouds. However, storing per-view feature maps rendered from distilled feature fields is impractical, and feature point clouds are expensive to store and query. To enable lightweight on-demand retrieval of pixel-aligned relevance masks, we introduce the Vector-Quantized Feature Field. We "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.06469","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/2503.06469/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":"2503.06469","created_at":"2026-07-05T10:27:34.504851+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.06469v1","created_at":"2026-07-05T10:27:34.504851+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.06469","created_at":"2026-07-05T10:27:34.504851+00:00"},{"alias_kind":"pith_short_12","alias_value":"VRKBSS2QP5YP","created_at":"2026-07-05T10:27:34.504851+00:00"},{"alias_kind":"pith_short_16","alias_value":"VRKBSS2QP5YPFLAN","created_at":"2026-07-05T10:27:34.504851+00:00"},{"alias_kind":"pith_short_8","alias_value":"VRKBSS2Q","created_at":"2026-07-05T10:27:34.504851+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/VRKBSS2QP5YPFLANDF2G6IUVXK","json":"https://pith.science/pith/VRKBSS2QP5YPFLANDF2G6IUVXK.json","graph_json":"https://pith.science/api/pith-number/VRKBSS2QP5YPFLANDF2G6IUVXK/graph.json","events_json":"https://pith.science/api/pith-number/VRKBSS2QP5YPFLANDF2G6IUVXK/events.json","paper":"https://pith.science/paper/VRKBSS2Q"},"agent_actions":{"view_html":"https://pith.science/pith/VRKBSS2QP5YPFLANDF2G6IUVXK","download_json":"https://pith.science/pith/VRKBSS2QP5YPFLANDF2G6IUVXK.json","view_paper":"https://pith.science/paper/VRKBSS2Q","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.06469&json=true","fetch_graph":"https://pith.science/api/pith-number/VRKBSS2QP5YPFLANDF2G6IUVXK/graph.json","fetch_events":"https://pith.science/api/pith-number/VRKBSS2QP5YPFLANDF2G6IUVXK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VRKBSS2QP5YPFLANDF2G6IUVXK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VRKBSS2QP5YPFLANDF2G6IUVXK/action/storage_attestation","attest_author":"https://pith.science/pith/VRKBSS2QP5YPFLANDF2G6IUVXK/action/author_attestation","sign_citation":"https://pith.science/pith/VRKBSS2QP5YPFLANDF2G6IUVXK/action/citation_signature","submit_replication":"https://pith.science/pith/VRKBSS2QP5YPFLANDF2G6IUVXK/action/replication_record"}},"created_at":"2026-07-05T10:27:34.504851+00:00","updated_at":"2026-07-05T10:27:34.504851+00:00"}