{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:2M54E7SZR42ERE6SAQULJ777YP","short_pith_number":"pith:2M54E7SZ","schema_version":"1.0","canonical_sha256":"d33bc27e598f344893d20428b4ffffc3fb7d79a7770d40f46e9f0595f647f4c2","source":{"kind":"arxiv","id":"2403.11831","version":2},"attestation_state":"computed","paper":{"title":"BAD-Gaussians: Bundle Adjusted Deblur Gaussian Splatting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Lingzhe Zhao, Peidong Liu, Peng Wang","submitted_at":"2024-03-18T14:43:04Z","abstract_excerpt":"While neural rendering has demonstrated impressive capabilities in 3D scene reconstruction and novel view synthesis, it heavily relies on high-quality sharp images and accurate camera poses. Numerous approaches have been proposed to train Neural Radiance Fields (NeRF) with motion-blurred images, commonly encountered in real-world scenarios such as low-light or long-exposure conditions. However, the implicit representation of NeRF struggles to accurately recover intricate details from severely motion-blurred images and cannot achieve real-time rendering. In contrast, recent advancements in 3D G"},"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":"2403.11831","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-03-18T14:43:04Z","cross_cats_sorted":[],"title_canon_sha256":"a4086733685607e0dcd99a28890244c0eb023e846f5dbd208eed600c3d25069c","abstract_canon_sha256":"e0bb5aeacec2bb20eb7de24a63a3e08f0984c543a8e7fb309c7da7cb380cc02a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:57:53.099703Z","signature_b64":"oqB01oIMBPsJysmU8QZ4DCG2a3wO9PjV8SVO27mLjOuGTPWAtX+nHDsr/L9w/VNt5g1YAxTpjiu3EgoTW76eCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d33bc27e598f344893d20428b4ffffc3fb7d79a7770d40f46e9f0595f647f4c2","last_reissued_at":"2026-07-05T07:57:53.099204Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:57:53.099204Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"BAD-Gaussians: Bundle Adjusted Deblur Gaussian Splatting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Lingzhe Zhao, Peidong Liu, Peng Wang","submitted_at":"2024-03-18T14:43:04Z","abstract_excerpt":"While neural rendering has demonstrated impressive capabilities in 3D scene reconstruction and novel view synthesis, it heavily relies on high-quality sharp images and accurate camera poses. Numerous approaches have been proposed to train Neural Radiance Fields (NeRF) with motion-blurred images, commonly encountered in real-world scenarios such as low-light or long-exposure conditions. However, the implicit representation of NeRF struggles to accurately recover intricate details from severely motion-blurred images and cannot achieve real-time rendering. In contrast, recent advancements in 3D G"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.11831","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/2403.11831/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":"2403.11831","created_at":"2026-07-05T07:57:53.099268+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.11831v2","created_at":"2026-07-05T07:57:53.099268+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.11831","created_at":"2026-07-05T07:57:53.099268+00:00"},{"alias_kind":"pith_short_12","alias_value":"2M54E7SZR42E","created_at":"2026-07-05T07:57:53.099268+00:00"},{"alias_kind":"pith_short_16","alias_value":"2M54E7SZR42ERE6S","created_at":"2026-07-05T07:57:53.099268+00:00"},{"alias_kind":"pith_short_8","alias_value":"2M54E7SZ","created_at":"2026-07-05T07:57:53.099268+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2401.03890","citing_title":"A Survey on 3D Gaussian Splatting","ref_index":300,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2M54E7SZR42ERE6SAQULJ777YP","json":"https://pith.science/pith/2M54E7SZR42ERE6SAQULJ777YP.json","graph_json":"https://pith.science/api/pith-number/2M54E7SZR42ERE6SAQULJ777YP/graph.json","events_json":"https://pith.science/api/pith-number/2M54E7SZR42ERE6SAQULJ777YP/events.json","paper":"https://pith.science/paper/2M54E7SZ"},"agent_actions":{"view_html":"https://pith.science/pith/2M54E7SZR42ERE6SAQULJ777YP","download_json":"https://pith.science/pith/2M54E7SZR42ERE6SAQULJ777YP.json","view_paper":"https://pith.science/paper/2M54E7SZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.11831&json=true","fetch_graph":"https://pith.science/api/pith-number/2M54E7SZR42ERE6SAQULJ777YP/graph.json","fetch_events":"https://pith.science/api/pith-number/2M54E7SZR42ERE6SAQULJ777YP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2M54E7SZR42ERE6SAQULJ777YP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2M54E7SZR42ERE6SAQULJ777YP/action/storage_attestation","attest_author":"https://pith.science/pith/2M54E7SZR42ERE6SAQULJ777YP/action/author_attestation","sign_citation":"https://pith.science/pith/2M54E7SZR42ERE6SAQULJ777YP/action/citation_signature","submit_replication":"https://pith.science/pith/2M54E7SZR42ERE6SAQULJ777YP/action/replication_record"}},"created_at":"2026-07-05T07:57:53.099268+00:00","updated_at":"2026-07-05T07:57:53.099268+00:00"}