{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:HTPYCGEUVK2OTQGXBG5VDYYSCT","short_pith_number":"pith:HTPYCGEU","schema_version":"1.0","canonical_sha256":"3cdf811894aab4e9c0d709bb51e31214ccf7919f3bb5e6f8819936da2d9df5f8","source":{"kind":"arxiv","id":"2303.05512","version":1},"attestation_state":"computed","paper":{"title":"PAC-NeRF: Physics Augmented Continuum Neural Radiance Fields for Geometry-Agnostic System Identification","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.AI","cs.GR","cs.LG","cs.RO"],"primary_cat":"cs.CV","authors_text":"Chenfanfu Jiang, Chuang Gan, Krishna Murthy Jatavallabhula, Ming Lin, Peter Yichen Chen, Xuan Li, Yi-Ling Qiao","submitted_at":"2023-03-09T18:59:50Z","abstract_excerpt":"Existing approaches to system identification (estimating the physical parameters of an object) from videos assume known object geometries. This precludes their applicability in a vast majority of scenes where object geometries are complex or unknown. In this work, we aim to identify parameters characterizing a physical system from a set of multi-view videos without any assumption on object geometry or topology. To this end, we propose \"Physics Augmented Continuum Neural Radiance Fields\" (PAC-NeRF), to estimate both the unknown geometry and physical parameters of highly dynamic objects from mul"},"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":"2303.05512","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CV","submitted_at":"2023-03-09T18:59:50Z","cross_cats_sorted":["cs.AI","cs.GR","cs.LG","cs.RO"],"title_canon_sha256":"0afbcecbd872583036647b82ae74d16d2ff60ba177a6cdb0ec7a15c6533a98a0","abstract_canon_sha256":"f29567fa92a7ace8a48f8ec2e2d7bb69cbac56f4ab5191843c03a5079a0f9a27"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:49:41.953716Z","signature_b64":"2XfAgZTjN0lugcN59NwXQ6zDizxfClOjYgoaDpCNA9palRwRXTMv1uWFeubz5TJ8iEyWKTlND6swgkECRRu5DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3cdf811894aab4e9c0d709bb51e31214ccf7919f3bb5e6f8819936da2d9df5f8","last_reissued_at":"2026-07-05T05:49:41.953205Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:49:41.953205Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PAC-NeRF: Physics Augmented Continuum Neural Radiance Fields for Geometry-Agnostic System Identification","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.AI","cs.GR","cs.LG","cs.RO"],"primary_cat":"cs.CV","authors_text":"Chenfanfu Jiang, Chuang Gan, Krishna Murthy Jatavallabhula, Ming Lin, Peter Yichen Chen, Xuan Li, Yi-Ling Qiao","submitted_at":"2023-03-09T18:59:50Z","abstract_excerpt":"Existing approaches to system identification (estimating the physical parameters of an object) from videos assume known object geometries. This precludes their applicability in a vast majority of scenes where object geometries are complex or unknown. In this work, we aim to identify parameters characterizing a physical system from a set of multi-view videos without any assumption on object geometry or topology. To this end, we propose \"Physics Augmented Continuum Neural Radiance Fields\" (PAC-NeRF), to estimate both the unknown geometry and physical parameters of highly dynamic objects from mul"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.05512","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/2303.05512/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":"2303.05512","created_at":"2026-07-05T05:49:41.953265+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.05512v1","created_at":"2026-07-05T05:49:41.953265+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.05512","created_at":"2026-07-05T05:49:41.953265+00:00"},{"alias_kind":"pith_short_12","alias_value":"HTPYCGEUVK2O","created_at":"2026-07-05T05:49:41.953265+00:00"},{"alias_kind":"pith_short_16","alias_value":"HTPYCGEUVK2OTQGX","created_at":"2026-07-05T05:49:41.953265+00:00"},{"alias_kind":"pith_short_8","alias_value":"HTPYCGEU","created_at":"2026-07-05T05:49:41.953265+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":9,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.23455","citing_title":"MeGAS: Thermomechanical Dynamic Gaussian Splatting for Thermophysical Scene Editing","ref_index":24,"is_internal_anchor":false},{"citing_arxiv_id":"2605.25909","citing_title":"R5DGS: Semantic-Aware 4D Gaussian Splatting with Rigid Body Constraints for Efficient Dynamic Scene Reconstruction","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2605.30347","citing_title":"NeuROK: Generative 4D Neural Object Kinematics","ref_index":56,"is_internal_anchor":false},{"citing_arxiv_id":"2605.30239","citing_title":"SAM3D-Phys: Towards Multi-Object Interactive Simulation in Real World","ref_index":21,"is_internal_anchor":false},{"citing_arxiv_id":"2412.09176","citing_title":"LIVE-GS: LLM Powers Interactive VR Experience with Physics-Aware Gaussian Splatting","ref_index":23,"is_internal_anchor":false},{"citing_arxiv_id":"2605.22597","citing_title":"MoSA: Motion-constrained Stress Adaptation for Mitigating Real-to-Sim Gap in Continuum Dynamics via Learning Residual Anisotropy","ref_index":10,"is_internal_anchor":false},{"citing_arxiv_id":"2605.09299","citing_title":"LagrangianSplats: Divergence-Free Transport of Gaussian Primitives for Fluid Reconstruction","ref_index":118,"is_internal_anchor":false},{"citing_arxiv_id":"2604.23574","citing_title":"PhysLayer: Language-Guided Layered Animation with Depth-Aware Physics","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2604.07882","citing_title":"ReconPhys: Reconstruct Appearance and Physical Attributes from Single Video","ref_index":16,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HTPYCGEUVK2OTQGXBG5VDYYSCT","json":"https://pith.science/pith/HTPYCGEUVK2OTQGXBG5VDYYSCT.json","graph_json":"https://pith.science/api/pith-number/HTPYCGEUVK2OTQGXBG5VDYYSCT/graph.json","events_json":"https://pith.science/api/pith-number/HTPYCGEUVK2OTQGXBG5VDYYSCT/events.json","paper":"https://pith.science/paper/HTPYCGEU"},"agent_actions":{"view_html":"https://pith.science/pith/HTPYCGEUVK2OTQGXBG5VDYYSCT","download_json":"https://pith.science/pith/HTPYCGEUVK2OTQGXBG5VDYYSCT.json","view_paper":"https://pith.science/paper/HTPYCGEU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.05512&json=true","fetch_graph":"https://pith.science/api/pith-number/HTPYCGEUVK2OTQGXBG5VDYYSCT/graph.json","fetch_events":"https://pith.science/api/pith-number/HTPYCGEUVK2OTQGXBG5VDYYSCT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HTPYCGEUVK2OTQGXBG5VDYYSCT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HTPYCGEUVK2OTQGXBG5VDYYSCT/action/storage_attestation","attest_author":"https://pith.science/pith/HTPYCGEUVK2OTQGXBG5VDYYSCT/action/author_attestation","sign_citation":"https://pith.science/pith/HTPYCGEUVK2OTQGXBG5VDYYSCT/action/citation_signature","submit_replication":"https://pith.science/pith/HTPYCGEUVK2OTQGXBG5VDYYSCT/action/replication_record"}},"created_at":"2026-07-05T05:49:41.953265+00:00","updated_at":"2026-07-05T05:49:41.953265+00:00"}