{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:2NNGKZ34N5CWFRKXZYZ53HKJLJ","short_pith_number":"pith:2NNGKZ34","schema_version":"1.0","canonical_sha256":"d35a65677c6f4562c557ce33dd9d495a5e89f46359a9d383703f6c225e919777","source":{"kind":"arxiv","id":"2404.06710","version":3},"attestation_state":"computed","paper":{"title":"SpikeNVS: Enhancing Novel View Synthesis from Blurry Images via Spike Camera","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Boxin Shi, Gaole Dai, Ming Lu, Qinwen Xu, Shanghang Zhang, Tiejun Huang, Wen Chen, Zhenyu Wang","submitted_at":"2024-04-10T03:31:32Z","abstract_excerpt":"One of the most critical factors in achieving sharp Novel View Synthesis (NVS) using neural field methods like Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) is the quality of the training images. However, Conventional RGB cameras are susceptible to motion blur. In contrast, neuromorphic cameras like event and spike cameras inherently capture more comprehensive temporal information, which can provide a sharp representation of the scene as additional training data. Recent methods have explored the integration of event cameras to improve the quality of NVS. The event-RGB approach"},"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":"2404.06710","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-04-10T03:31:32Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"e4e361d315028b891396ee2259f98053aadfd36bd521f431983a151a90f7a5ea","abstract_canon_sha256":"7e9f4cf6fba8e9b0631668e1c689d15115f2c9f09ec951aad045230111015d23"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:07:16.761671Z","signature_b64":"rzTkXt0AmqgJ3p98N0wSv87u9FeZaqQSVcKnsS5udEKQCmBD2vXxcR2F7E2zy/V6LvUJq9EyWjJEp+56u132CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d35a65677c6f4562c557ce33dd9d495a5e89f46359a9d383703f6c225e919777","last_reissued_at":"2026-07-05T08:07:16.761225Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:07:16.761225Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SpikeNVS: Enhancing Novel View Synthesis from Blurry Images via Spike Camera","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Boxin Shi, Gaole Dai, Ming Lu, Qinwen Xu, Shanghang Zhang, Tiejun Huang, Wen Chen, Zhenyu Wang","submitted_at":"2024-04-10T03:31:32Z","abstract_excerpt":"One of the most critical factors in achieving sharp Novel View Synthesis (NVS) using neural field methods like Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) is the quality of the training images. However, Conventional RGB cameras are susceptible to motion blur. In contrast, neuromorphic cameras like event and spike cameras inherently capture more comprehensive temporal information, which can provide a sharp representation of the scene as additional training data. Recent methods have explored the integration of event cameras to improve the quality of NVS. The event-RGB approach"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.06710","kind":"arxiv","version":3},"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/2404.06710/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":"2404.06710","created_at":"2026-07-05T08:07:16.761282+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.06710v3","created_at":"2026-07-05T08:07:16.761282+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.06710","created_at":"2026-07-05T08:07:16.761282+00:00"},{"alias_kind":"pith_short_12","alias_value":"2NNGKZ34N5CW","created_at":"2026-07-05T08:07:16.761282+00:00"},{"alias_kind":"pith_short_16","alias_value":"2NNGKZ34N5CWFRKX","created_at":"2026-07-05T08:07:16.761282+00:00"},{"alias_kind":"pith_short_8","alias_value":"2NNGKZ34","created_at":"2026-07-05T08:07:16.761282+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.16151","citing_title":"SPACT18: Spiking Human Action Recognition Benchmark Dataset with Complementary RGB and Thermal Modalities","ref_index":15,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2NNGKZ34N5CWFRKXZYZ53HKJLJ","json":"https://pith.science/pith/2NNGKZ34N5CWFRKXZYZ53HKJLJ.json","graph_json":"https://pith.science/api/pith-number/2NNGKZ34N5CWFRKXZYZ53HKJLJ/graph.json","events_json":"https://pith.science/api/pith-number/2NNGKZ34N5CWFRKXZYZ53HKJLJ/events.json","paper":"https://pith.science/paper/2NNGKZ34"},"agent_actions":{"view_html":"https://pith.science/pith/2NNGKZ34N5CWFRKXZYZ53HKJLJ","download_json":"https://pith.science/pith/2NNGKZ34N5CWFRKXZYZ53HKJLJ.json","view_paper":"https://pith.science/paper/2NNGKZ34","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.06710&json=true","fetch_graph":"https://pith.science/api/pith-number/2NNGKZ34N5CWFRKXZYZ53HKJLJ/graph.json","fetch_events":"https://pith.science/api/pith-number/2NNGKZ34N5CWFRKXZYZ53HKJLJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2NNGKZ34N5CWFRKXZYZ53HKJLJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2NNGKZ34N5CWFRKXZYZ53HKJLJ/action/storage_attestation","attest_author":"https://pith.science/pith/2NNGKZ34N5CWFRKXZYZ53HKJLJ/action/author_attestation","sign_citation":"https://pith.science/pith/2NNGKZ34N5CWFRKXZYZ53HKJLJ/action/citation_signature","submit_replication":"https://pith.science/pith/2NNGKZ34N5CWFRKXZYZ53HKJLJ/action/replication_record"}},"created_at":"2026-07-05T08:07:16.761282+00:00","updated_at":"2026-07-05T08:07:16.761282+00:00"}