{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:VBBTE4S3VHYFUXVHK2DVG57YNS","short_pith_number":"pith:VBBTE4S3","schema_version":"1.0","canonical_sha256":"a84332725ba9f05a5ea756875377f86c8af62f065fc9dd2b48b3774236bc5614","source":{"kind":"arxiv","id":"2508.20965","version":1},"attestation_state":"computed","paper":{"title":"DrivingGaussian++: Towards Realistic Reconstruction and Editable Simulation for Surrounding Dynamic Driving Scenes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Deqing Sun, Ming-Hsuan Yang, Xiaoyu Zhou, Yajiao Xiong, Yongtao Wan","submitted_at":"2025-08-28T16:22:54Z","abstract_excerpt":"We present DrivingGaussian++, an efficient and effective framework for realistic reconstructing and controllable editing of surrounding dynamic autonomous driving scenes. DrivingGaussian++ models the static background using incremental 3D Gaussians and reconstructs moving objects with a composite dynamic Gaussian graph, ensuring accurate positions and occlusions. By integrating a LiDAR prior, it achieves detailed and consistent scene reconstruction, outperforming existing methods in dynamic scene reconstruction and photorealistic surround-view synthesis. DrivingGaussian++ supports training-fre"},"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":"2508.20965","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-08-28T16:22:54Z","cross_cats_sorted":[],"title_canon_sha256":"645e0ee07ac8b6b56bd7b3f72bd149c6e4bd999da63cdc6857f646717247de8b","abstract_canon_sha256":"0015a6c5f8f126f7d6f3e671e4c57aa4e486fa223e9ba7440ee09e3e581a7ed3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:01:14.264783Z","signature_b64":"NltsttYGzf6eEIta9btlLLrY6pseBWfwxnOnyN4HXBtHxTUmpsEiWtGGXJ/MH5Ib1P/I+mX4egnoWQkhtFbFAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a84332725ba9f05a5ea756875377f86c8af62f065fc9dd2b48b3774236bc5614","last_reissued_at":"2026-07-05T12:01:14.264274Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:01:14.264274Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DrivingGaussian++: Towards Realistic Reconstruction and Editable Simulation for Surrounding Dynamic Driving Scenes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Deqing Sun, Ming-Hsuan Yang, Xiaoyu Zhou, Yajiao Xiong, Yongtao Wan","submitted_at":"2025-08-28T16:22:54Z","abstract_excerpt":"We present DrivingGaussian++, an efficient and effective framework for realistic reconstructing and controllable editing of surrounding dynamic autonomous driving scenes. DrivingGaussian++ models the static background using incremental 3D Gaussians and reconstructs moving objects with a composite dynamic Gaussian graph, ensuring accurate positions and occlusions. By integrating a LiDAR prior, it achieves detailed and consistent scene reconstruction, outperforming existing methods in dynamic scene reconstruction and photorealistic surround-view synthesis. DrivingGaussian++ supports training-fre"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.20965","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/2508.20965/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":"2508.20965","created_at":"2026-07-05T12:01:14.264342+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.20965v1","created_at":"2026-07-05T12:01:14.264342+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.20965","created_at":"2026-07-05T12:01:14.264342+00:00"},{"alias_kind":"pith_short_12","alias_value":"VBBTE4S3VHYF","created_at":"2026-07-05T12:01:14.264342+00:00"},{"alias_kind":"pith_short_16","alias_value":"VBBTE4S3VHYFUXVH","created_at":"2026-07-05T12:01:14.264342+00:00"},{"alias_kind":"pith_short_8","alias_value":"VBBTE4S3","created_at":"2026-07-05T12:01:14.264342+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.25373","citing_title":"Physics-Aware 3D Gaussian Editing for Driving Scene Generation","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2512.17445","citing_title":"LangDriveCTRL: Natural Language Controllable Driving Scene Editing with Multi-modal Agents","ref_index":54,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VBBTE4S3VHYFUXVHK2DVG57YNS","json":"https://pith.science/pith/VBBTE4S3VHYFUXVHK2DVG57YNS.json","graph_json":"https://pith.science/api/pith-number/VBBTE4S3VHYFUXVHK2DVG57YNS/graph.json","events_json":"https://pith.science/api/pith-number/VBBTE4S3VHYFUXVHK2DVG57YNS/events.json","paper":"https://pith.science/paper/VBBTE4S3"},"agent_actions":{"view_html":"https://pith.science/pith/VBBTE4S3VHYFUXVHK2DVG57YNS","download_json":"https://pith.science/pith/VBBTE4S3VHYFUXVHK2DVG57YNS.json","view_paper":"https://pith.science/paper/VBBTE4S3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.20965&json=true","fetch_graph":"https://pith.science/api/pith-number/VBBTE4S3VHYFUXVHK2DVG57YNS/graph.json","fetch_events":"https://pith.science/api/pith-number/VBBTE4S3VHYFUXVHK2DVG57YNS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VBBTE4S3VHYFUXVHK2DVG57YNS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VBBTE4S3VHYFUXVHK2DVG57YNS/action/storage_attestation","attest_author":"https://pith.science/pith/VBBTE4S3VHYFUXVHK2DVG57YNS/action/author_attestation","sign_citation":"https://pith.science/pith/VBBTE4S3VHYFUXVHK2DVG57YNS/action/citation_signature","submit_replication":"https://pith.science/pith/VBBTE4S3VHYFUXVHK2DVG57YNS/action/replication_record"}},"created_at":"2026-07-05T12:01:14.264342+00:00","updated_at":"2026-07-05T12:01:14.264342+00:00"}