{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:MLD2ZSGL4T467RJBYZYJONOYGI","short_pith_number":"pith:MLD2ZSGL","canonical_record":{"source":{"id":"2312.17655","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-29T15:44:13Z","cross_cats_sorted":[],"title_canon_sha256":"8f0b136e55b708c7884ef9472215817acc69ebce50e30a251950045207f05562","abstract_canon_sha256":"6ff5998f71b630828e612df2245f266a65bcd685a37c5f68f5c927c25775e459"},"schema_version":"1.0"},"canonical_sha256":"62c7acc8cbe4f9efc521c6709735d8322c13da5b8c03150b7070dc6d4ecf636f","source":{"kind":"arxiv","id":"2312.17655","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.17655","created_at":"2026-07-05T07:28:54Z"},{"alias_kind":"arxiv_version","alias_value":"2312.17655v1","created_at":"2026-07-05T07:28:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.17655","created_at":"2026-07-05T07:28:54Z"},{"alias_kind":"pith_short_12","alias_value":"MLD2ZSGL4T46","created_at":"2026-07-05T07:28:54Z"},{"alias_kind":"pith_short_16","alias_value":"MLD2ZSGL4T467RJB","created_at":"2026-07-05T07:28:54Z"},{"alias_kind":"pith_short_8","alias_value":"MLD2ZSGL","created_at":"2026-07-05T07:28:54Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:MLD2ZSGL4T467RJBYZYJONOYGI","target":"record","payload":{"canonical_record":{"source":{"id":"2312.17655","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-29T15:44:13Z","cross_cats_sorted":[],"title_canon_sha256":"8f0b136e55b708c7884ef9472215817acc69ebce50e30a251950045207f05562","abstract_canon_sha256":"6ff5998f71b630828e612df2245f266a65bcd685a37c5f68f5c927c25775e459"},"schema_version":"1.0"},"canonical_sha256":"62c7acc8cbe4f9efc521c6709735d8322c13da5b8c03150b7070dc6d4ecf636f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:28:54.977377Z","signature_b64":"7YoOeExL6WikkbmJSxeCXi9PAkuzWsio2hvFhyqBgQ2ZZp9XLeI/2e93HfdwOJXRdFaV2eVqyHhYyT5It3rcBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"62c7acc8cbe4f9efc521c6709735d8322c13da5b8c03150b7070dc6d4ecf636f","last_reissued_at":"2026-07-05T07:28:54.976900Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:28:54.976900Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2312.17655","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:28:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9adGHLDhhlqscM/dzp8WhCbO8HS/Nhf+k+sKQMg59h3QDMZRsxj+KuoBASX7ZMTS8JP/DYLN/G65CwEjcyXOCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T09:59:48.885440Z"},"content_sha256":"dd8b68153055124201dfcf466500617d474cefa708873f7066d63d50e08a54da","schema_version":"1.0","event_id":"sha256:dd8b68153055124201dfcf466500617d474cefa708873f7066d63d50e08a54da"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:MLD2ZSGL4T467RJBYZYJONOYGI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Visual Point Cloud Forecasting enables Scalable Autonomous Driving","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hongyang Li, Li Chen, Yanan Sun, Zetong Yang","submitted_at":"2023-12-29T15:44:13Z","abstract_excerpt":"In contrast to extensive studies on general vision, pre-training for scalable visual autonomous driving remains seldom explored. Visual autonomous driving applications require features encompassing semantics, 3D geometry, and temporal information simultaneously for joint perception, prediction, and planning, posing dramatic challenges for pre-training. To resolve this, we bring up a new pre-training task termed as visual point cloud forecasting - predicting future point clouds from historical visual input. The key merit of this task captures the synergic learning of semantics, 3D structures, a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.17655","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/2312.17655/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:28:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wigrV96XqtvSJ/h9XcjeJpwkIE49jJmKvB5Dy0BykeP+dh2Q/O+Jz1wcuwvjLCB+7qH1dOrAGoqupI7O0dgrBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T09:59:48.885916Z"},"content_sha256":"1f4b157f1dce34b9419cd805b682a19659f0bf5053ae748c42c4e8da4709fb48","schema_version":"1.0","event_id":"sha256:1f4b157f1dce34b9419cd805b682a19659f0bf5053ae748c42c4e8da4709fb48"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MLD2ZSGL4T467RJBYZYJONOYGI/bundle.json","state_url":"https://pith.science/pith/MLD2ZSGL4T467RJBYZYJONOYGI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MLD2ZSGL4T467RJBYZYJONOYGI/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":"2026-08-04T09:59:48Z","links":{"resolver":"https://pith.science/pith/MLD2ZSGL4T467RJBYZYJONOYGI","bundle":"https://pith.science/pith/MLD2ZSGL4T467RJBYZYJONOYGI/bundle.json","state":"https://pith.science/pith/MLD2ZSGL4T467RJBYZYJONOYGI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MLD2ZSGL4T467RJBYZYJONOYGI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:MLD2ZSGL4T467RJBYZYJONOYGI","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":"6ff5998f71b630828e612df2245f266a65bcd685a37c5f68f5c927c25775e459","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-29T15:44:13Z","title_canon_sha256":"8f0b136e55b708c7884ef9472215817acc69ebce50e30a251950045207f05562"},"schema_version":"1.0","source":{"id":"2312.17655","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.17655","created_at":"2026-07-05T07:28:54Z"},{"alias_kind":"arxiv_version","alias_value":"2312.17655v1","created_at":"2026-07-05T07:28:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.17655","created_at":"2026-07-05T07:28:54Z"},{"alias_kind":"pith_short_12","alias_value":"MLD2ZSGL4T46","created_at":"2026-07-05T07:28:54Z"},{"alias_kind":"pith_short_16","alias_value":"MLD2ZSGL4T467RJB","created_at":"2026-07-05T07:28:54Z"},{"alias_kind":"pith_short_8","alias_value":"MLD2ZSGL","created_at":"2026-07-05T07:28:54Z"}],"graph_snapshots":[{"event_id":"sha256:1f4b157f1dce34b9419cd805b682a19659f0bf5053ae748c42c4e8da4709fb48","target":"graph","created_at":"2026-07-05T07:28:54Z","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/2312.17655/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In contrast to extensive studies on general vision, pre-training for scalable visual autonomous driving remains seldom explored. Visual autonomous driving applications require features encompassing semantics, 3D geometry, and temporal information simultaneously for joint perception, prediction, and planning, posing dramatic challenges for pre-training. To resolve this, we bring up a new pre-training task termed as visual point cloud forecasting - predicting future point clouds from historical visual input. The key merit of this task captures the synergic learning of semantics, 3D structures, a","authors_text":"Hongyang Li, Li Chen, Yanan Sun, Zetong Yang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-29T15:44:13Z","title":"Visual Point Cloud Forecasting enables Scalable Autonomous Driving"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.17655","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:dd8b68153055124201dfcf466500617d474cefa708873f7066d63d50e08a54da","target":"record","created_at":"2026-07-05T07:28:54Z","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":"6ff5998f71b630828e612df2245f266a65bcd685a37c5f68f5c927c25775e459","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-29T15:44:13Z","title_canon_sha256":"8f0b136e55b708c7884ef9472215817acc69ebce50e30a251950045207f05562"},"schema_version":"1.0","source":{"id":"2312.17655","kind":"arxiv","version":1}},"canonical_sha256":"62c7acc8cbe4f9efc521c6709735d8322c13da5b8c03150b7070dc6d4ecf636f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"62c7acc8cbe4f9efc521c6709735d8322c13da5b8c03150b7070dc6d4ecf636f","first_computed_at":"2026-07-05T07:28:54.976900Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:28:54.976900Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7YoOeExL6WikkbmJSxeCXi9PAkuzWsio2hvFhyqBgQ2ZZp9XLeI/2e93HfdwOJXRdFaV2eVqyHhYyT5It3rcBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:28:54.977377Z","signed_message":"canonical_sha256_bytes"},"source_id":"2312.17655","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dd8b68153055124201dfcf466500617d474cefa708873f7066d63d50e08a54da","sha256:1f4b157f1dce34b9419cd805b682a19659f0bf5053ae748c42c4e8da4709fb48"],"state_sha256":"52d77fed008aaedb9aa2ba918dd2ac084d5a9b31db0661cee9cf255e3425237e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HO+6lgRlitNH0PceeS0KWYV9LiCZJ2+YbitulS6kxys5YQVh5BCh3cAKrsBxlAxhs6RDJNkl9q8jU9NmY/+tBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T09:59:48.890146Z","bundle_sha256":"f415a885a8658261059c80535415fb342930548aba28b672fd3e9b8f469eaa11"}}