{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:LEOPS543VDLK3YKGQAHOALL3Z7","short_pith_number":"pith:LEOPS543","schema_version":"1.0","canonical_sha256":"591cf9779ba8d6ade146800ee02d7bcfcb2a3a5631d41cce4bf575931cd13dba","source":{"kind":"arxiv","id":"2511.12972","version":2},"attestation_state":"computed","paper":{"title":"SplatSearch: Instance Image Goal Navigation for Mobile Robots using 3D Gaussian Splatting and Diffusion Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Goldie Nejat, Haitong Wang, Matthew Lisondra, Siddarth Narasimhan","submitted_at":"2025-11-17T04:49:28Z","abstract_excerpt":"The Instance Image Goal Navigation (IIN) problem requires mobile robots deployed in unknown environments to search for specific objects or people of interest using only a single reference goal image of the target. This problem can be especially challenging when: 1) the reference image is captured from an arbitrary viewpoint, and 2) the robot must operate with sparse-view scene reconstructions. In this paper, we address the IIN problem, by introducing SplatSearch, a novel architecture that leverages sparse-view 3D Gaussian Splatting (3DGS) reconstructions. SplatSearch renders multiple viewpoint"},"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":"2511.12972","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-11-17T04:49:28Z","cross_cats_sorted":[],"title_canon_sha256":"9fd009e54206e32ba1953d300583228e0745716191501fe5cda1db5fcdc3c88d","abstract_canon_sha256":"0a9569c921c9f4b281dd15be09a5d24f603d12285675c60644347cbb2e91e541"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-21T01:21:40.824409Z","signature_b64":"AiDh+F5XsgJRqnZV27EotRrz/7wS6f0D5ERExP6+xpfTUMI5xUTYm1CTvcH65y2LsZ+8GFAho9ogywXBGoNtCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"591cf9779ba8d6ade146800ee02d7bcfcb2a3a5631d41cce4bf575931cd13dba","last_reissued_at":"2026-07-21T01:21:40.823476Z","signature_status":"signed_v1","first_computed_at":"2026-07-21T01:21:40.823476Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SplatSearch: Instance Image Goal Navigation for Mobile Robots using 3D Gaussian Splatting and Diffusion Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Goldie Nejat, Haitong Wang, Matthew Lisondra, Siddarth Narasimhan","submitted_at":"2025-11-17T04:49:28Z","abstract_excerpt":"The Instance Image Goal Navigation (IIN) problem requires mobile robots deployed in unknown environments to search for specific objects or people of interest using only a single reference goal image of the target. This problem can be especially challenging when: 1) the reference image is captured from an arbitrary viewpoint, and 2) the robot must operate with sparse-view scene reconstructions. In this paper, we address the IIN problem, by introducing SplatSearch, a novel architecture that leverages sparse-view 3D Gaussian Splatting (3DGS) reconstructions. SplatSearch renders multiple viewpoint"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2511.12972","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/2511.12972/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":"2511.12972","created_at":"2026-07-21T01:21:40.823912+00:00"},{"alias_kind":"arxiv_version","alias_value":"2511.12972v2","created_at":"2026-07-21T01:21:40.823912+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2511.12972","created_at":"2026-07-21T01:21:40.823912+00:00"},{"alias_kind":"pith_short_12","alias_value":"LEOPS543VDLK","created_at":"2026-07-21T01:21:40.823912+00:00"},{"alias_kind":"pith_short_16","alias_value":"LEOPS543VDLK3YKG","created_at":"2026-07-21T01:21:40.823912+00:00"},{"alias_kind":"pith_short_8","alias_value":"LEOPS543","created_at":"2026-07-21T01:21:40.823912+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2604.05351","citing_title":"AnyImageNav: Any-View Geometry for Precise Last-Meter Image-Goal Navigation","ref_index":20,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LEOPS543VDLK3YKGQAHOALL3Z7","json":"https://pith.science/pith/LEOPS543VDLK3YKGQAHOALL3Z7.json","graph_json":"https://pith.science/api/pith-number/LEOPS543VDLK3YKGQAHOALL3Z7/graph.json","events_json":"https://pith.science/api/pith-number/LEOPS543VDLK3YKGQAHOALL3Z7/events.json","paper":"https://pith.science/paper/LEOPS543"},"agent_actions":{"view_html":"https://pith.science/pith/LEOPS543VDLK3YKGQAHOALL3Z7","download_json":"https://pith.science/pith/LEOPS543VDLK3YKGQAHOALL3Z7.json","view_paper":"https://pith.science/paper/LEOPS543","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2511.12972&json=true","fetch_graph":"https://pith.science/api/pith-number/LEOPS543VDLK3YKGQAHOALL3Z7/graph.json","fetch_events":"https://pith.science/api/pith-number/LEOPS543VDLK3YKGQAHOALL3Z7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LEOPS543VDLK3YKGQAHOALL3Z7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LEOPS543VDLK3YKGQAHOALL3Z7/action/storage_attestation","attest_author":"https://pith.science/pith/LEOPS543VDLK3YKGQAHOALL3Z7/action/author_attestation","sign_citation":"https://pith.science/pith/LEOPS543VDLK3YKGQAHOALL3Z7/action/citation_signature","submit_replication":"https://pith.science/pith/LEOPS543VDLK3YKGQAHOALL3Z7/action/replication_record"}},"created_at":"2026-07-21T01:21:40.823912+00:00","updated_at":"2026-07-21T01:21:40.823912+00:00"}