{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:YW4OK63EAPVRZFCPFADCQFBFW6","short_pith_number":"pith:YW4OK63E","schema_version":"1.0","canonical_sha256":"c5b8e57b6403eb1c944f2806281425b7ad9aa89e19caf3bf684a0dfb8062ff76","source":{"kind":"arxiv","id":"2506.03872","version":1},"attestation_state":"computed","paper":{"title":"JointSplat: Probabilistic Joint Flow-Depth Optimization for Sparse-View Gaussian Splatting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Guoan Xu, Qiang Wu, Wenjing Jia, Yang Xiao","submitted_at":"2025-06-04T12:04:40Z","abstract_excerpt":"Reconstructing 3D scenes from sparse viewpoints is a long-standing challenge with wide applications. Recent advances in feed-forward 3D Gaussian sparse-view reconstruction methods provide an efficient solution for real-time novel view synthesis by leveraging geometric priors learned from large-scale multi-view datasets and computing 3D Gaussian centers via back-projection. Despite offering strong geometric cues, both feed-forward multi-view depth estimation and flow-depth joint estimation face key limitations: the former suffers from mislocation and artifact issues in low-texture or repetitive"},"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":"2506.03872","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-04T12:04:40Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"c5ccddf54b4dd9990a6ddd97b5ae84a0e7f1fdbb139ea3e2e95d7958515a30b7","abstract_canon_sha256":"4561fa84b88711bc0265a5a41e3b900eec19c3ac99ddf3dc80968d2568eeacb4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:15:53.158519Z","signature_b64":"FAqBEgsnyQmTTu7JcIMIL14lV/BfPTpx69/LeLxmCBmhH1rka4K8H86MkpaLMdw3mc7hPAC95H+lygqYXbPIBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c5b8e57b6403eb1c944f2806281425b7ad9aa89e19caf3bf684a0dfb8062ff76","last_reissued_at":"2026-07-05T11:15:53.157979Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:15:53.157979Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"JointSplat: Probabilistic Joint Flow-Depth Optimization for Sparse-View Gaussian Splatting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Guoan Xu, Qiang Wu, Wenjing Jia, Yang Xiao","submitted_at":"2025-06-04T12:04:40Z","abstract_excerpt":"Reconstructing 3D scenes from sparse viewpoints is a long-standing challenge with wide applications. Recent advances in feed-forward 3D Gaussian sparse-view reconstruction methods provide an efficient solution for real-time novel view synthesis by leveraging geometric priors learned from large-scale multi-view datasets and computing 3D Gaussian centers via back-projection. Despite offering strong geometric cues, both feed-forward multi-view depth estimation and flow-depth joint estimation face key limitations: the former suffers from mislocation and artifact issues in low-texture or repetitive"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.03872","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/2506.03872/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":"2506.03872","created_at":"2026-07-05T11:15:53.158050+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.03872v1","created_at":"2026-07-05T11:15:53.158050+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.03872","created_at":"2026-07-05T11:15:53.158050+00:00"},{"alias_kind":"pith_short_12","alias_value":"YW4OK63EAPVR","created_at":"2026-07-05T11:15:53.158050+00:00"},{"alias_kind":"pith_short_16","alias_value":"YW4OK63EAPVRZFCP","created_at":"2026-07-05T11:15:53.158050+00:00"},{"alias_kind":"pith_short_8","alias_value":"YW4OK63E","created_at":"2026-07-05T11:15:53.158050+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.26115","citing_title":"TriSplat: Simulation-Ready Feed-Forward 3D Scene Reconstruction","ref_index":60,"is_internal_anchor":false},{"citing_arxiv_id":"2604.14025","citing_title":"Feed-Forward 3D Scene Modeling: A Problem-Driven Perspective","ref_index":160,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YW4OK63EAPVRZFCPFADCQFBFW6","json":"https://pith.science/pith/YW4OK63EAPVRZFCPFADCQFBFW6.json","graph_json":"https://pith.science/api/pith-number/YW4OK63EAPVRZFCPFADCQFBFW6/graph.json","events_json":"https://pith.science/api/pith-number/YW4OK63EAPVRZFCPFADCQFBFW6/events.json","paper":"https://pith.science/paper/YW4OK63E"},"agent_actions":{"view_html":"https://pith.science/pith/YW4OK63EAPVRZFCPFADCQFBFW6","download_json":"https://pith.science/pith/YW4OK63EAPVRZFCPFADCQFBFW6.json","view_paper":"https://pith.science/paper/YW4OK63E","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.03872&json=true","fetch_graph":"https://pith.science/api/pith-number/YW4OK63EAPVRZFCPFADCQFBFW6/graph.json","fetch_events":"https://pith.science/api/pith-number/YW4OK63EAPVRZFCPFADCQFBFW6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YW4OK63EAPVRZFCPFADCQFBFW6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YW4OK63EAPVRZFCPFADCQFBFW6/action/storage_attestation","attest_author":"https://pith.science/pith/YW4OK63EAPVRZFCPFADCQFBFW6/action/author_attestation","sign_citation":"https://pith.science/pith/YW4OK63EAPVRZFCPFADCQFBFW6/action/citation_signature","submit_replication":"https://pith.science/pith/YW4OK63EAPVRZFCPFADCQFBFW6/action/replication_record"}},"created_at":"2026-07-05T11:15:53.158050+00:00","updated_at":"2026-07-05T11:15:53.158050+00:00"}