{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:62QVYDV4QUS67WSHEZHY2PIXLY","short_pith_number":"pith:62QVYDV4","schema_version":"1.0","canonical_sha256":"f6a15c0ebc8525efda47264f8d3d175e0c2d4d5d07d1b8c391066be8dc0408d5","source":{"kind":"arxiv","id":"2410.08743","version":1},"attestation_state":"computed","paper":{"title":"Look Gauss, No Pose: Novel View Synthesis using Gaussian Splatting without Accurate Pose Initialization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bastian Leibe, Christian Schmidt, Jens Piekenbrinck","submitted_at":"2024-10-11T12:01:15Z","abstract_excerpt":"3D Gaussian Splatting has recently emerged as a powerful tool for fast and accurate novel-view synthesis from a set of posed input images. However, like most novel-view synthesis approaches, it relies on accurate camera pose information, limiting its applicability in real-world scenarios where acquiring accurate camera poses can be challenging or even impossible. We propose an extension to the 3D Gaussian Splatting framework by optimizing the extrinsic camera parameters with respect to photometric residuals. We derive the analytical gradients and integrate their computation with the existing h"},"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":"2410.08743","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-10-11T12:01:15Z","cross_cats_sorted":[],"title_canon_sha256":"f38e5512f71df298a095d49b9a578e46afb74a6c74967668d9e6df0fc2db0c41","abstract_canon_sha256":"342ad7b2be33064402f087140ca5217fd6a29c3caece3c5672f428dc7fb8e0f5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:19:16.950896Z","signature_b64":"jcUrpiQH6LUJqaAbf4yWPyuiK5l/ModY6HNLkUa7YLCzME7iIGqbKGvEKniNc1QHYsGZCj/WGhMmIMxvCFz9Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f6a15c0ebc8525efda47264f8d3d175e0c2d4d5d07d1b8c391066be8dc0408d5","last_reissued_at":"2026-07-05T09:19:16.950471Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:19:16.950471Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Look Gauss, No Pose: Novel View Synthesis using Gaussian Splatting without Accurate Pose Initialization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bastian Leibe, Christian Schmidt, Jens Piekenbrinck","submitted_at":"2024-10-11T12:01:15Z","abstract_excerpt":"3D Gaussian Splatting has recently emerged as a powerful tool for fast and accurate novel-view synthesis from a set of posed input images. However, like most novel-view synthesis approaches, it relies on accurate camera pose information, limiting its applicability in real-world scenarios where acquiring accurate camera poses can be challenging or even impossible. We propose an extension to the 3D Gaussian Splatting framework by optimizing the extrinsic camera parameters with respect to photometric residuals. We derive the analytical gradients and integrate their computation with the existing h"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.08743","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/2410.08743/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":"2410.08743","created_at":"2026-07-05T09:19:16.950532+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.08743v1","created_at":"2026-07-05T09:19:16.950532+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.08743","created_at":"2026-07-05T09:19:16.950532+00:00"},{"alias_kind":"pith_short_12","alias_value":"62QVYDV4QUS6","created_at":"2026-07-05T09:19:16.950532+00:00"},{"alias_kind":"pith_short_16","alias_value":"62QVYDV4QUS67WSH","created_at":"2026-07-05T09:19:16.950532+00:00"},{"alias_kind":"pith_short_8","alias_value":"62QVYDV4","created_at":"2026-07-05T09:19:16.950532+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.16932","citing_title":"GSemSplat: Generalizable Semantic 3D Gaussian Splatting from Uncalibrated Image Pairs","ref_index":29,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/62QVYDV4QUS67WSHEZHY2PIXLY","json":"https://pith.science/pith/62QVYDV4QUS67WSHEZHY2PIXLY.json","graph_json":"https://pith.science/api/pith-number/62QVYDV4QUS67WSHEZHY2PIXLY/graph.json","events_json":"https://pith.science/api/pith-number/62QVYDV4QUS67WSHEZHY2PIXLY/events.json","paper":"https://pith.science/paper/62QVYDV4"},"agent_actions":{"view_html":"https://pith.science/pith/62QVYDV4QUS67WSHEZHY2PIXLY","download_json":"https://pith.science/pith/62QVYDV4QUS67WSHEZHY2PIXLY.json","view_paper":"https://pith.science/paper/62QVYDV4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.08743&json=true","fetch_graph":"https://pith.science/api/pith-number/62QVYDV4QUS67WSHEZHY2PIXLY/graph.json","fetch_events":"https://pith.science/api/pith-number/62QVYDV4QUS67WSHEZHY2PIXLY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/62QVYDV4QUS67WSHEZHY2PIXLY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/62QVYDV4QUS67WSHEZHY2PIXLY/action/storage_attestation","attest_author":"https://pith.science/pith/62QVYDV4QUS67WSHEZHY2PIXLY/action/author_attestation","sign_citation":"https://pith.science/pith/62QVYDV4QUS67WSHEZHY2PIXLY/action/citation_signature","submit_replication":"https://pith.science/pith/62QVYDV4QUS67WSHEZHY2PIXLY/action/replication_record"}},"created_at":"2026-07-05T09:19:16.950532+00:00","updated_at":"2026-07-05T09:19:16.950532+00:00"}