{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:EI3SJA4QVLBDFQTKFOH2FMJEXN","short_pith_number":"pith:EI3SJA4Q","canonical_record":{"source":{"id":"2512.18954","kind":"arxiv","version":6},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-12-22T02:05:45Z","cross_cats_sorted":[],"title_canon_sha256":"fa3c62e3185c209586a2e33983e1952dd1444423abc9b513d9fced709162884c","abstract_canon_sha256":"b6d6ee9ca2d4af5f716f8744fd28e6bfc98ad0d998db1db2a3f8f9404dcbeda4"},"schema_version":"1.0"},"canonical_sha256":"2237248390aac232c26a2b8fa2b124bb5d171729ba86180544fb9d6a5ee20cb4","source":{"kind":"arxiv","id":"2512.18954","version":6},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2512.18954","created_at":"2026-06-03T01:05:47Z"},{"alias_kind":"arxiv_version","alias_value":"2512.18954v6","created_at":"2026-06-03T01:05:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2512.18954","created_at":"2026-06-03T01:05:47Z"},{"alias_kind":"pith_short_12","alias_value":"EI3SJA4QVLBD","created_at":"2026-06-03T01:05:47Z"},{"alias_kind":"pith_short_16","alias_value":"EI3SJA4QVLBDFQTK","created_at":"2026-06-03T01:05:47Z"},{"alias_kind":"pith_short_8","alias_value":"EI3SJA4Q","created_at":"2026-06-03T01:05:47Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:EI3SJA4QVLBDFQTKFOH2FMJEXN","target":"record","payload":{"canonical_record":{"source":{"id":"2512.18954","kind":"arxiv","version":6},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-12-22T02:05:45Z","cross_cats_sorted":[],"title_canon_sha256":"fa3c62e3185c209586a2e33983e1952dd1444423abc9b513d9fced709162884c","abstract_canon_sha256":"b6d6ee9ca2d4af5f716f8744fd28e6bfc98ad0d998db1db2a3f8f9404dcbeda4"},"schema_version":"1.0"},"canonical_sha256":"2237248390aac232c26a2b8fa2b124bb5d171729ba86180544fb9d6a5ee20cb4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-03T01:05:47.017508Z","signature_b64":"nHt+g0p8PsLzm9CbcAwLrv43oJSQO/8AFfuxw68IALmzyohdz7LjuvRXECExe6j1IDMI0XBdEjg50x0zwsYWBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2237248390aac232c26a2b8fa2b124bb5d171729ba86180544fb9d6a5ee20cb4","last_reissued_at":"2026-06-03T01:05:47.017104Z","signature_status":"signed_v1","first_computed_at":"2026-06-03T01:05:47.017104Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2512.18954","source_version":6,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-03T01:05:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3Ih1mSjcHxpigwsJ4lskrBP9p5es4kqqxwKEdE6LQ6dev4zyLdWVYS0hajQfYs75LTqjt0edYkpHi0/LgJMdDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T17:08:58.073460Z"},"content_sha256":"965f03f7ab0d2f0974392f320a652ce4fed020b00a2a59f53abb6a05f62ba5f1","schema_version":"1.0","event_id":"sha256:965f03f7ab0d2f0974392f320a652ce4fed020b00a2a59f53abb6a05f62ba5f1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:EI3SJA4QVLBDFQTKFOH2FMJEXN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"VOIC: Visible-Occluded Integrated Guidance for 3D Semantic Scene Completion","license":"http://creativecommons.org/licenses/by/4.0/","headline":"A dual-decoder network separates visible and occluded region supervision to improve monocular 3D semantic scene completion.","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jiang Liu, Risa Higashita, Zaidao Han","submitted_at":"2025-12-22T02:05:45Z","abstract_excerpt":"Camera-based 3D Semantic Scene Completion (SSC) is a critical task for autonomous driving and robotic scene understanding. It aims to infer a complete 3D volumetric representation of both semantics and geometry from a single image. Existing methods typically focus on end-to-end 2D-to-3D feature lifting and voxel completion. However, they often overlook the interference between high-confidence visible-region perception and low-confidence occluded-region reasoning caused by single-image input, which can lead to feature dilution and error propagation.\n  To address these challenges, we introduce a"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"VOIC outperforms existing monocular SSC methods in both geometric completion and semantic segmentation accuracy, achieving state-of-the-art performance on the SemanticKITTI and SSCBench-KITTI360 benchmarks.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The assumption that offline extraction of visible-region voxel labels from dense 3D ground truth cleanly separates supervision without introducing selection bias or losing critical information needed for coherent global reasoning.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"VOIC decouples monocular 3D scene completion into visible semantic perception and occluded reasoning via VRLE and a dual-decoder architecture, achieving state-of-the-art results on SemanticKITTI and SSCBench-KITTI360.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A dual-decoder network separates visible and occluded region supervision to improve monocular 3D semantic scene completion.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"2b0d49e3e466cc87352bbc68b461122cd52c8fdb586564ea63fbf696282d15df"},"source":{"id":"2512.18954","kind":"arxiv","version":6},"verdict":{"id":"052b3ca3-e4bb-4a6e-b2e5-cbde302cd0e4","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-16T20:31:46.478229Z","strongest_claim":"VOIC outperforms existing monocular SSC methods in both geometric completion and semantic segmentation accuracy, achieving state-of-the-art performance on the SemanticKITTI and SSCBench-KITTI360 benchmarks.","one_line_summary":"VOIC decouples monocular 3D scene completion into visible semantic perception and occluded reasoning via VRLE and a dual-decoder architecture, achieving state-of-the-art results on SemanticKITTI and SSCBench-KITTI360.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The assumption that offline extraction of visible-region voxel labels from dense 3D ground truth cleanly separates supervision without introducing selection bias or losing critical information needed for coherent global reasoning.","pith_extraction_headline":"A dual-decoder network separates visible and occluded region supervision to improve monocular 3D semantic scene completion."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2512.18954/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":2,"snapshot_sha256":"c63d8ddd510ff37ddbb65b0756445b495a2e30e8a8e76d42bd71d09cf2ed0392"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"052b3ca3-e4bb-4a6e-b2e5-cbde302cd0e4"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-03T01:05:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CjuTJeRVGdTczt4SkC/5gtXML1kS8Kfh4Zv4autRABLwXFBqTQ+PGBgVdetmH271o9mi42r6xLdaKoDixbGSCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T17:08:58.074228Z"},"content_sha256":"c3b86a78e8bab8424f345622ca2bf9ba9c271e7cae7c18eb835be9038ae415f5","schema_version":"1.0","event_id":"sha256:c3b86a78e8bab8424f345622ca2bf9ba9c271e7cae7c18eb835be9038ae415f5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EI3SJA4QVLBDFQTKFOH2FMJEXN/bundle.json","state_url":"https://pith.science/pith/EI3SJA4QVLBDFQTKFOH2FMJEXN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EI3SJA4QVLBDFQTKFOH2FMJEXN/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-07T17:08:58Z","links":{"resolver":"https://pith.science/pith/EI3SJA4QVLBDFQTKFOH2FMJEXN","bundle":"https://pith.science/pith/EI3SJA4QVLBDFQTKFOH2FMJEXN/bundle.json","state":"https://pith.science/pith/EI3SJA4QVLBDFQTKFOH2FMJEXN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EI3SJA4QVLBDFQTKFOH2FMJEXN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:EI3SJA4QVLBDFQTKFOH2FMJEXN","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"b6d6ee9ca2d4af5f716f8744fd28e6bfc98ad0d998db1db2a3f8f9404dcbeda4","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-12-22T02:05:45Z","title_canon_sha256":"fa3c62e3185c209586a2e33983e1952dd1444423abc9b513d9fced709162884c"},"schema_version":"1.0","source":{"id":"2512.18954","kind":"arxiv","version":6}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2512.18954","created_at":"2026-06-03T01:05:47Z"},{"alias_kind":"arxiv_version","alias_value":"2512.18954v6","created_at":"2026-06-03T01:05:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2512.18954","created_at":"2026-06-03T01:05:47Z"},{"alias_kind":"pith_short_12","alias_value":"EI3SJA4QVLBD","created_at":"2026-06-03T01:05:47Z"},{"alias_kind":"pith_short_16","alias_value":"EI3SJA4QVLBDFQTK","created_at":"2026-06-03T01:05:47Z"},{"alias_kind":"pith_short_8","alias_value":"EI3SJA4Q","created_at":"2026-06-03T01:05:47Z"}],"graph_snapshots":[{"event_id":"sha256:c3b86a78e8bab8424f345622ca2bf9ba9c271e7cae7c18eb835be9038ae415f5","target":"graph","created_at":"2026-06-03T01:05:47Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"VOIC outperforms existing monocular SSC methods in both geometric completion and semantic segmentation accuracy, achieving state-of-the-art performance on the SemanticKITTI and SSCBench-KITTI360 benchmarks."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The assumption that offline extraction of visible-region voxel labels from dense 3D ground truth cleanly separates supervision without introducing selection bias or losing critical information needed for coherent global reasoning."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"VOIC decouples monocular 3D scene completion into visible semantic perception and occluded reasoning via VRLE and a dual-decoder architecture, achieving state-of-the-art results on SemanticKITTI and SSCBench-KITTI360."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A dual-decoder network separates visible and occluded region supervision to improve monocular 3D semantic scene completion."}],"snapshot_sha256":"2b0d49e3e466cc87352bbc68b461122cd52c8fdb586564ea63fbf696282d15df"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"c63d8ddd510ff37ddbb65b0756445b495a2e30e8a8e76d42bd71d09cf2ed0392"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2512.18954/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Camera-based 3D Semantic Scene Completion (SSC) is a critical task for autonomous driving and robotic scene understanding. It aims to infer a complete 3D volumetric representation of both semantics and geometry from a single image. Existing methods typically focus on end-to-end 2D-to-3D feature lifting and voxel completion. However, they often overlook the interference between high-confidence visible-region perception and low-confidence occluded-region reasoning caused by single-image input, which can lead to feature dilution and error propagation.\n  To address these challenges, we introduce a","authors_text":"Jiang Liu, Risa Higashita, Zaidao Han","cross_cats":[],"headline":"A dual-decoder network separates visible and occluded region supervision to improve monocular 3D semantic scene completion.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-12-22T02:05:45Z","title":"VOIC: Visible-Occluded Integrated Guidance for 3D Semantic Scene Completion"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2512.18954","kind":"arxiv","version":6},"verdict":{"created_at":"2026-05-16T20:31:46.478229Z","id":"052b3ca3-e4bb-4a6e-b2e5-cbde302cd0e4","model_set":{"reader":"grok-4.3"},"one_line_summary":"VOIC decouples monocular 3D scene completion into visible semantic perception and occluded reasoning via VRLE and a dual-decoder architecture, achieving state-of-the-art results on SemanticKITTI and SSCBench-KITTI360.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A dual-decoder network separates visible and occluded region supervision to improve monocular 3D semantic scene completion.","strongest_claim":"VOIC outperforms existing monocular SSC methods in both geometric completion and semantic segmentation accuracy, achieving state-of-the-art performance on the SemanticKITTI and SSCBench-KITTI360 benchmarks.","weakest_assumption":"The assumption that offline extraction of visible-region voxel labels from dense 3D ground truth cleanly separates supervision without introducing selection bias or losing critical information needed for coherent global reasoning."}},"verdict_id":"052b3ca3-e4bb-4a6e-b2e5-cbde302cd0e4"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:965f03f7ab0d2f0974392f320a652ce4fed020b00a2a59f53abb6a05f62ba5f1","target":"record","created_at":"2026-06-03T01:05:47Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"b6d6ee9ca2d4af5f716f8744fd28e6bfc98ad0d998db1db2a3f8f9404dcbeda4","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-12-22T02:05:45Z","title_canon_sha256":"fa3c62e3185c209586a2e33983e1952dd1444423abc9b513d9fced709162884c"},"schema_version":"1.0","source":{"id":"2512.18954","kind":"arxiv","version":6}},"canonical_sha256":"2237248390aac232c26a2b8fa2b124bb5d171729ba86180544fb9d6a5ee20cb4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2237248390aac232c26a2b8fa2b124bb5d171729ba86180544fb9d6a5ee20cb4","first_computed_at":"2026-06-03T01:05:47.017104Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-03T01:05:47.017104Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"nHt+g0p8PsLzm9CbcAwLrv43oJSQO/8AFfuxw68IALmzyohdz7LjuvRXECExe6j1IDMI0XBdEjg50x0zwsYWBA==","signature_status":"signed_v1","signed_at":"2026-06-03T01:05:47.017508Z","signed_message":"canonical_sha256_bytes"},"source_id":"2512.18954","source_kind":"arxiv","source_version":6}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:965f03f7ab0d2f0974392f320a652ce4fed020b00a2a59f53abb6a05f62ba5f1","sha256:c3b86a78e8bab8424f345622ca2bf9ba9c271e7cae7c18eb835be9038ae415f5"],"state_sha256":"54c95947d5c4e722e4680728950054ed8a420824034ea2e715da5c4e19f90438"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KRdKO9z+8BJ59QTf7g0hXV0BmVgNB6ylQswbMMH3bHt0wqmTx5IF0N8LEZZLEwcQq9Dj2ggMIIKWYdP15jTIBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T17:08:58.079817Z","bundle_sha256":"2492f7984cf7030e393dbd1f0fc4836375e69c59276fff60d9900bb93978fc05"}}