{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:JQEQN4IMWXBEU4WAAXS6F4FRDT","short_pith_number":"pith:JQEQN4IM","schema_version":"1.0","canonical_sha256":"4c0906f10cb5c24a72c005e5e2f0b11cdf177711a640b57d101bf28a6e38390c","source":{"kind":"arxiv","id":"2406.03723","version":1},"attestation_state":"computed","paper":{"title":"Gear-NeRF: Free-Viewpoint Rendering and Tracking with Motion-aware Spatio-Temporal Sampling","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.GR","cs.MM"],"primary_cat":"cs.CV","authors_text":"Chi-Keung Tang, Moitreya Chatterjee, Pedro Miraldo, Suhas Lohit, Xinhang Liu, Yu-Wing Tai","submitted_at":"2024-06-06T03:37:39Z","abstract_excerpt":"Extensions of Neural Radiance Fields (NeRFs) to model dynamic scenes have enabled their near photo-realistic, free-viewpoint rendering. Although these methods have shown some potential in creating immersive experiences, two drawbacks limit their ubiquity: (i) a significant reduction in reconstruction quality when the computing budget is limited, and (ii) a lack of semantic understanding of the underlying scenes. To address these issues, we introduce Gear-NeRF, which leverages semantic information from powerful image segmentation models. Our approach presents a principled way for learning a spa"},"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":"2406.03723","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-06-06T03:37:39Z","cross_cats_sorted":["cs.GR","cs.MM"],"title_canon_sha256":"7f238564f959c0fb0569880c3b12efe33a32f8148e8c07940abcee9f267d50af","abstract_canon_sha256":"e932c15e657ba705f9f5ed937804b6842018c491803cd6850e0987c1f5313f57"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:28:15.316124Z","signature_b64":"qhuDM6DztB8+wv5ThIpd2EWSOvplxyUD8ombfWVEyl3DGe/Ex8YWDLf2Ujl7BJ4Dz76Kdi7Sg8mp3rbEL1EdDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4c0906f10cb5c24a72c005e5e2f0b11cdf177711a640b57d101bf28a6e38390c","last_reissued_at":"2026-07-05T08:28:15.315637Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:28:15.315637Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Gear-NeRF: Free-Viewpoint Rendering and Tracking with Motion-aware Spatio-Temporal Sampling","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.GR","cs.MM"],"primary_cat":"cs.CV","authors_text":"Chi-Keung Tang, Moitreya Chatterjee, Pedro Miraldo, Suhas Lohit, Xinhang Liu, Yu-Wing Tai","submitted_at":"2024-06-06T03:37:39Z","abstract_excerpt":"Extensions of Neural Radiance Fields (NeRFs) to model dynamic scenes have enabled their near photo-realistic, free-viewpoint rendering. Although these methods have shown some potential in creating immersive experiences, two drawbacks limit their ubiquity: (i) a significant reduction in reconstruction quality when the computing budget is limited, and (ii) a lack of semantic understanding of the underlying scenes. To address these issues, we introduce Gear-NeRF, which leverages semantic information from powerful image segmentation models. Our approach presents a principled way for learning a spa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.03723","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/2406.03723/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":"2406.03723","created_at":"2026-07-05T08:28:15.315703+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.03723v1","created_at":"2026-07-05T08:28:15.315703+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.03723","created_at":"2026-07-05T08:28:15.315703+00:00"},{"alias_kind":"pith_short_12","alias_value":"JQEQN4IMWXBE","created_at":"2026-07-05T08:28:15.315703+00:00"},{"alias_kind":"pith_short_16","alias_value":"JQEQN4IMWXBEU4WA","created_at":"2026-07-05T08:28:15.315703+00:00"},{"alias_kind":"pith_short_8","alias_value":"JQEQN4IM","created_at":"2026-07-05T08:28:15.315703+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/JQEQN4IMWXBEU4WAAXS6F4FRDT","json":"https://pith.science/pith/JQEQN4IMWXBEU4WAAXS6F4FRDT.json","graph_json":"https://pith.science/api/pith-number/JQEQN4IMWXBEU4WAAXS6F4FRDT/graph.json","events_json":"https://pith.science/api/pith-number/JQEQN4IMWXBEU4WAAXS6F4FRDT/events.json","paper":"https://pith.science/paper/JQEQN4IM"},"agent_actions":{"view_html":"https://pith.science/pith/JQEQN4IMWXBEU4WAAXS6F4FRDT","download_json":"https://pith.science/pith/JQEQN4IMWXBEU4WAAXS6F4FRDT.json","view_paper":"https://pith.science/paper/JQEQN4IM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.03723&json=true","fetch_graph":"https://pith.science/api/pith-number/JQEQN4IMWXBEU4WAAXS6F4FRDT/graph.json","fetch_events":"https://pith.science/api/pith-number/JQEQN4IMWXBEU4WAAXS6F4FRDT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JQEQN4IMWXBEU4WAAXS6F4FRDT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JQEQN4IMWXBEU4WAAXS6F4FRDT/action/storage_attestation","attest_author":"https://pith.science/pith/JQEQN4IMWXBEU4WAAXS6F4FRDT/action/author_attestation","sign_citation":"https://pith.science/pith/JQEQN4IMWXBEU4WAAXS6F4FRDT/action/citation_signature","submit_replication":"https://pith.science/pith/JQEQN4IMWXBEU4WAAXS6F4FRDT/action/replication_record"}},"created_at":"2026-07-05T08:28:15.315703+00:00","updated_at":"2026-07-05T08:28:15.315703+00:00"}