{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:F5CRZHGY2JKD6C4EJAX53CIMQV","short_pith_number":"pith:F5CRZHGY","schema_version":"1.0","canonical_sha256":"2f451c9cd8d2543f0b84482fdd890c8540e0b5877ffde09c88a0e77d59a9efa3","source":{"kind":"arxiv","id":"2603.06210","version":2},"attestation_state":"computed","paper":{"title":"VG3S: Visual Geometry Grounded Gaussian Splatting for Semantic Occupancy Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Muleilan Pei, Shaojie Shen, Xiaoyang Yan","submitted_at":"2026-03-06T12:26:47Z","abstract_excerpt":"3D semantic occupancy prediction has become a crucial perception task for comprehensive scene understanding in autonomous driving. While recent advances have explored 3D Gaussian splatting for occupancy modeling to substantially reduce computational overhead, the generation of high-quality 3D Gaussians relies heavily on accurate geometric cues, which are often insufficient in purely vision-centric paradigms. To bridge this gap, we advocate for injecting the strong geometric grounding capability from Vision Foundation Models (VFMs) into occupancy prediction. In this regard, we introduce Visual "},"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":"2603.06210","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-03-06T12:26:47Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"3212b4b5b1d5b5af4e9c8a80fb033be3aafbcf01b2de580a7fff1a20446f58d6","abstract_canon_sha256":"2751818cd94d593369454ca6d5ff5deb4f93eab070c24328a4290a8963db0907"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-23T01:23:34.400709Z","signature_b64":"RnpL7+PPWb1WRh3Tdl8uDVQHM5ZLU4Q6xKFvbNvFMyci3HPBH73bfz3ceDda/xnotgCqLEwGEQIueYLbkkBNBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2f451c9cd8d2543f0b84482fdd890c8540e0b5877ffde09c88a0e77d59a9efa3","last_reissued_at":"2026-07-23T01:23:34.399593Z","signature_status":"signed_v1","first_computed_at":"2026-07-23T01:23:34.399593Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"VG3S: Visual Geometry Grounded Gaussian Splatting for Semantic Occupancy Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Muleilan Pei, Shaojie Shen, Xiaoyang Yan","submitted_at":"2026-03-06T12:26:47Z","abstract_excerpt":"3D semantic occupancy prediction has become a crucial perception task for comprehensive scene understanding in autonomous driving. While recent advances have explored 3D Gaussian splatting for occupancy modeling to substantially reduce computational overhead, the generation of high-quality 3D Gaussians relies heavily on accurate geometric cues, which are often insufficient in purely vision-centric paradigms. To bridge this gap, we advocate for injecting the strong geometric grounding capability from Vision Foundation Models (VFMs) into occupancy prediction. In this regard, we introduce Visual "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2603.06210","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/2603.06210/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":"2603.06210","created_at":"2026-07-23T01:23:34.400142+00:00"},{"alias_kind":"arxiv_version","alias_value":"2603.06210v2","created_at":"2026-07-23T01:23:34.400142+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2603.06210","created_at":"2026-07-23T01:23:34.400142+00:00"},{"alias_kind":"pith_short_12","alias_value":"F5CRZHGY2JKD","created_at":"2026-07-23T01:23:34.400142+00:00"},{"alias_kind":"pith_short_16","alias_value":"F5CRZHGY2JKD6C4E","created_at":"2026-07-23T01:23:34.400142+00:00"},{"alias_kind":"pith_short_8","alias_value":"F5CRZHGY","created_at":"2026-07-23T01:23:34.400142+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2608.08696","citing_title":"OccAnyScene: Towards Unified Indoor-Outdoor 3D Occupancy Prediction","ref_index":29,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/F5CRZHGY2JKD6C4EJAX53CIMQV","json":"https://pith.science/pith/F5CRZHGY2JKD6C4EJAX53CIMQV.json","graph_json":"https://pith.science/api/pith-number/F5CRZHGY2JKD6C4EJAX53CIMQV/graph.json","events_json":"https://pith.science/api/pith-number/F5CRZHGY2JKD6C4EJAX53CIMQV/events.json","paper":"https://pith.science/paper/F5CRZHGY"},"agent_actions":{"view_html":"https://pith.science/pith/F5CRZHGY2JKD6C4EJAX53CIMQV","download_json":"https://pith.science/pith/F5CRZHGY2JKD6C4EJAX53CIMQV.json","view_paper":"https://pith.science/paper/F5CRZHGY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2603.06210&json=true","fetch_graph":"https://pith.science/api/pith-number/F5CRZHGY2JKD6C4EJAX53CIMQV/graph.json","fetch_events":"https://pith.science/api/pith-number/F5CRZHGY2JKD6C4EJAX53CIMQV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/F5CRZHGY2JKD6C4EJAX53CIMQV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/F5CRZHGY2JKD6C4EJAX53CIMQV/action/storage_attestation","attest_author":"https://pith.science/pith/F5CRZHGY2JKD6C4EJAX53CIMQV/action/author_attestation","sign_citation":"https://pith.science/pith/F5CRZHGY2JKD6C4EJAX53CIMQV/action/citation_signature","submit_replication":"https://pith.science/pith/F5CRZHGY2JKD6C4EJAX53CIMQV/action/replication_record"}},"created_at":"2026-07-23T01:23:34.400142+00:00","updated_at":"2026-07-23T01:23:34.400142+00:00"}