{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:27QY4M5UMCGF7DPV3PCWDXQRWC","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":"ee7c4c689a14c1fd9a360ad60a9c2296ffb760e6519061b3d4b0d5b50770790e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-17T07:26:11Z","title_canon_sha256":"6013f73e1d5f8d2eb81587f01a1177185a163d95145c9ea6cf0659f089473a54"},"schema_version":"1.0","source":{"id":"2504.12709","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.12709","created_at":"2026-07-05T10:50:21Z"},{"alias_kind":"arxiv_version","alias_value":"2504.12709v1","created_at":"2026-07-05T10:50:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.12709","created_at":"2026-07-05T10:50:21Z"},{"alias_kind":"pith_short_12","alias_value":"27QY4M5UMCGF","created_at":"2026-07-05T10:50:21Z"},{"alias_kind":"pith_short_16","alias_value":"27QY4M5UMCGF7DPV","created_at":"2026-07-05T10:50:21Z"},{"alias_kind":"pith_short_8","alias_value":"27QY4M5U","created_at":"2026-07-05T10:50:21Z"}],"graph_snapshots":[{"event_id":"sha256:ddb446b83981899b37f9524e5a3f9b46f1a36fd0dd61098e4cdaace418878c1c","target":"graph","created_at":"2026-07-05T10:50:21Z","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/2504.12709/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The significant achievements of pre-trained models leveraging large volumes of data in the field of NLP and 2D vision inspire us to explore the potential of extensive data pre-training for 3D perception in autonomous driving. Toward this goal, this paper proposes to utilize massive unlabeled data from heterogeneous datasets to pre-train 3D perception models. We introduce a self-supervised pre-training framework that learns effective 3D representations from scratch on unlabeled data, combined with a prompt adapter based domain adaptation strategy to reduce dataset bias. The approach significant","authors_text":"Heng Li, Jianmin Ji, Jie Peng, Lehan Pan, Lidian Wang, Sha Zhang, Shumin Wang, Yanyong Zhang, Zhipeng Tang, Zhuoran Yang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-17T07:26:11Z","title":"Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.12709","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":"sha256:7c28b78f46d6798bb841820de57a55f092324b0b313a9c4a0f7dc5c2828a4118","target":"record","created_at":"2026-07-05T10:50:21Z","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":"ee7c4c689a14c1fd9a360ad60a9c2296ffb760e6519061b3d4b0d5b50770790e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-17T07:26:11Z","title_canon_sha256":"6013f73e1d5f8d2eb81587f01a1177185a163d95145c9ea6cf0659f089473a54"},"schema_version":"1.0","source":{"id":"2504.12709","kind":"arxiv","version":1}},"canonical_sha256":"d7e18e33b4608c5f8df5dbc561de11b08c2cc62f5fef691178db894abde24527","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d7e18e33b4608c5f8df5dbc561de11b08c2cc62f5fef691178db894abde24527","first_computed_at":"2026-07-05T10:50:21.094719Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:50:21.094719Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xZt9S2ExJOslV0piaQJfCFJQoWbIGkaviQ1Ggb/XX+0l3LsIA8zvTDlkF759Zsl1MTHhHhIh8lFmBmBW+yBdCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:50:21.095503Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.12709","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7c28b78f46d6798bb841820de57a55f092324b0b313a9c4a0f7dc5c2828a4118","sha256:ddb446b83981899b37f9524e5a3f9b46f1a36fd0dd61098e4cdaace418878c1c"],"state_sha256":"e65b90c646956df4877ef44f608902dee07e59991f477b4713fcdcbf7f772566"}