{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:RC72AAZYFN6K7DXBXXC7LH4SMZ","short_pith_number":"pith:RC72AAZY","schema_version":"1.0","canonical_sha256":"88bfa003382b7caf8ee1bdc5f59f926663577851f0dbf965c45b848850da6d18","source":{"kind":"arxiv","id":"2410.22817","version":2},"attestation_state":"computed","paper":{"title":"Epipolar-Free 3D Gaussian Splatting for Generalizable Novel View Synthesis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jianwen Sun, Yawei Luo, Yi Yang, Zhiyuan Min","submitted_at":"2024-10-30T08:51:29Z","abstract_excerpt":"Generalizable 3D Gaussian splitting (3DGS) can reconstruct new scenes from sparse-view observations in a feed-forward inference manner, eliminating the need for scene-specific retraining required in conventional 3DGS. However, existing methods rely heavily on epipolar priors, which can be unreliable in complex realworld scenes, particularly in non-overlapping and occluded regions. In this paper, we propose eFreeSplat, an efficient feed-forward 3DGS-based model for generalizable novel view synthesis that operates independently of epipolar line constraints. To enhance multiview feature extractio"},"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":"2410.22817","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-10-30T08:51:29Z","cross_cats_sorted":[],"title_canon_sha256":"31681d1e105cc0a5256ce2565135136f38cd9661102d597d5a9010cbb54cc42c","abstract_canon_sha256":"cfdca11219ecdf9df9e86f7fbd11c618f1d645067f5f27f01edfeb4fa059c441"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:28:57.599599Z","signature_b64":"laoXxI+dguR5lIiSB6srRbYVr9g4r+e2Wpxgnft3Qyp7p7xVWiez5TgnEc19EBl2EaYcDmNi7lq5AONWWCKeDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"88bfa003382b7caf8ee1bdc5f59f926663577851f0dbf965c45b848850da6d18","last_reissued_at":"2026-07-05T09:28:57.599111Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:28:57.599111Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Epipolar-Free 3D Gaussian Splatting for Generalizable Novel View Synthesis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jianwen Sun, Yawei Luo, Yi Yang, Zhiyuan Min","submitted_at":"2024-10-30T08:51:29Z","abstract_excerpt":"Generalizable 3D Gaussian splitting (3DGS) can reconstruct new scenes from sparse-view observations in a feed-forward inference manner, eliminating the need for scene-specific retraining required in conventional 3DGS. However, existing methods rely heavily on epipolar priors, which can be unreliable in complex realworld scenes, particularly in non-overlapping and occluded regions. In this paper, we propose eFreeSplat, an efficient feed-forward 3DGS-based model for generalizable novel view synthesis that operates independently of epipolar line constraints. To enhance multiview feature extractio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.22817","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/2410.22817/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":"2410.22817","created_at":"2026-07-05T09:28:57.599178+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.22817v2","created_at":"2026-07-05T09:28:57.599178+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.22817","created_at":"2026-07-05T09:28:57.599178+00:00"},{"alias_kind":"pith_short_12","alias_value":"RC72AAZYFN6K","created_at":"2026-07-05T09:28:57.599178+00:00"},{"alias_kind":"pith_short_16","alias_value":"RC72AAZYFN6K7DXB","created_at":"2026-07-05T09:28:57.599178+00:00"},{"alias_kind":"pith_short_8","alias_value":"RC72AAZY","created_at":"2026-07-05T09:28:57.599178+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.09479","citing_title":"TinySplat: Feedforward Approach for Generating Compact 3D Scene Representation","ref_index":50,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RC72AAZYFN6K7DXBXXC7LH4SMZ","json":"https://pith.science/pith/RC72AAZYFN6K7DXBXXC7LH4SMZ.json","graph_json":"https://pith.science/api/pith-number/RC72AAZYFN6K7DXBXXC7LH4SMZ/graph.json","events_json":"https://pith.science/api/pith-number/RC72AAZYFN6K7DXBXXC7LH4SMZ/events.json","paper":"https://pith.science/paper/RC72AAZY"},"agent_actions":{"view_html":"https://pith.science/pith/RC72AAZYFN6K7DXBXXC7LH4SMZ","download_json":"https://pith.science/pith/RC72AAZYFN6K7DXBXXC7LH4SMZ.json","view_paper":"https://pith.science/paper/RC72AAZY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.22817&json=true","fetch_graph":"https://pith.science/api/pith-number/RC72AAZYFN6K7DXBXXC7LH4SMZ/graph.json","fetch_events":"https://pith.science/api/pith-number/RC72AAZYFN6K7DXBXXC7LH4SMZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RC72AAZYFN6K7DXBXXC7LH4SMZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RC72AAZYFN6K7DXBXXC7LH4SMZ/action/storage_attestation","attest_author":"https://pith.science/pith/RC72AAZYFN6K7DXBXXC7LH4SMZ/action/author_attestation","sign_citation":"https://pith.science/pith/RC72AAZYFN6K7DXBXXC7LH4SMZ/action/citation_signature","submit_replication":"https://pith.science/pith/RC72AAZYFN6K7DXBXXC7LH4SMZ/action/replication_record"}},"created_at":"2026-07-05T09:28:57.599178+00:00","updated_at":"2026-07-05T09:28:57.599178+00:00"}