{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:ABVMPGOWHDWYY36ME7XOPLJ67F","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":"f24b7eb4d3d0dab6417aedde07aa3266da5d17b52cd5bea5336756186d4815d0","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-09-08T15:29:49Z","title_canon_sha256":"45b925af1b0528af7a29a9c3d2835bd6fd5beee225f8cb9952938c063d83e4d7"},"schema_version":"1.0","source":{"id":"2509.06798","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.06798","created_at":"2026-07-05T12:06:53Z"},{"alias_kind":"arxiv_version","alias_value":"2509.06798v1","created_at":"2026-07-05T12:06:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.06798","created_at":"2026-07-05T12:06:53Z"},{"alias_kind":"pith_short_12","alias_value":"ABVMPGOWHDWY","created_at":"2026-07-05T12:06:53Z"},{"alias_kind":"pith_short_16","alias_value":"ABVMPGOWHDWYY36M","created_at":"2026-07-05T12:06:53Z"},{"alias_kind":"pith_short_8","alias_value":"ABVMPGOW","created_at":"2026-07-05T12:06:53Z"}],"graph_snapshots":[{"event_id":"sha256:39733e91d760693cb06241509f2288c3cd7bd747ac705f38ee9293a77af49497","target":"graph","created_at":"2026-07-05T12:06:53Z","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":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2509.06798/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In the field of autonomous driving, sensor simulation is essential for generating rare and diverse scenarios that are difficult to capture in real-world environments. Current solutions fall into two categories: 1) CG-based methods, such as CARLA, which lack diversity and struggle to scale to the vast array of rare cases required for robust perception training; and 2) learning-based approaches, such as NeuSim, which are limited to specific object categories (vehicles) and require extensive multi-sensor data, hindering their applicability to generic objects. To address these limitations, we prop","authors_text":"Qian Zhang, Qingjie Wang, Ruohong Mei, Weiqiang Ren, Wei Yin, Xiaoyang Guo, Yubin Hu, Zhengqing Chen","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-09-08T15:29:49Z","title":"SynthDrive: Scalable Real2Sim2Real Sensor Simulation Pipeline for High-Fidelity Asset Generation and Driving Data Synthesis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.06798","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:829565c0e3fc23991ad5b0cc92ef14070e8cd3c4b54b9aa568fc1559c5b6cb46","target":"record","created_at":"2026-07-05T12:06:53Z","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":"f24b7eb4d3d0dab6417aedde07aa3266da5d17b52cd5bea5336756186d4815d0","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-09-08T15:29:49Z","title_canon_sha256":"45b925af1b0528af7a29a9c3d2835bd6fd5beee225f8cb9952938c063d83e4d7"},"schema_version":"1.0","source":{"id":"2509.06798","kind":"arxiv","version":1}},"canonical_sha256":"006ac799d638ed8c6fcc27eee7ad3ef95aaed5836a35623f38572c47fedda0fc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"006ac799d638ed8c6fcc27eee7ad3ef95aaed5836a35623f38572c47fedda0fc","first_computed_at":"2026-07-05T12:06:53.255484Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T12:06:53.255484Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"lxIHtnta+nV3YisNREjhiudNV5i97mGOaTfrMEZskhwmYPiVCWfbn6RyKYkOohg4bLSn/M5833vVqz6XvTPPDA==","signature_status":"signed_v1","signed_at":"2026-07-05T12:06:53.255911Z","signed_message":"canonical_sha256_bytes"},"source_id":"2509.06798","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:829565c0e3fc23991ad5b0cc92ef14070e8cd3c4b54b9aa568fc1559c5b6cb46","sha256:39733e91d760693cb06241509f2288c3cd7bd747ac705f38ee9293a77af49497"],"state_sha256":"e410335ae1b694ed1fd70cbaeeed4da811298641085050739d9707395e91e2c0"}