{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:HR5LCWQF3FGSJ347OKURQUGLXJ","short_pith_number":"pith:HR5LCWQF","canonical_record":{"source":{"id":"2109.00179","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-09-01T04:17:11Z","cross_cats_sorted":[],"title_canon_sha256":"905882fcefc9ac803c294fce4d0a717fb1277851e211d15dd030bc691ad2f94c","abstract_canon_sha256":"8b63690c266baa93f04918a7bc778a68aca97c61b7a8b1b7895186dd0aa66024"},"schema_version":"1.0"},"canonical_sha256":"3c7ab15a05d94d24ef9f72a91850cbba65ddac0995f4e4aca2353702f6f2bcae","source":{"kind":"arxiv","id":"2109.00179","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.00179","created_at":"2026-07-05T03:10:43Z"},{"alias_kind":"arxiv_version","alias_value":"2109.00179v1","created_at":"2026-07-05T03:10:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.00179","created_at":"2026-07-05T03:10:43Z"},{"alias_kind":"pith_short_12","alias_value":"HR5LCWQF3FGS","created_at":"2026-07-05T03:10:43Z"},{"alias_kind":"pith_short_16","alias_value":"HR5LCWQF3FGSJ347","created_at":"2026-07-05T03:10:43Z"},{"alias_kind":"pith_short_8","alias_value":"HR5LCWQF","created_at":"2026-07-05T03:10:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:HR5LCWQF3FGSJ347OKURQUGLXJ","target":"record","payload":{"canonical_record":{"source":{"id":"2109.00179","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-09-01T04:17:11Z","cross_cats_sorted":[],"title_canon_sha256":"905882fcefc9ac803c294fce4d0a717fb1277851e211d15dd030bc691ad2f94c","abstract_canon_sha256":"8b63690c266baa93f04918a7bc778a68aca97c61b7a8b1b7895186dd0aa66024"},"schema_version":"1.0"},"canonical_sha256":"3c7ab15a05d94d24ef9f72a91850cbba65ddac0995f4e4aca2353702f6f2bcae","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:10:43.545585Z","signature_b64":"JWx8bHQb/6HT9TLkuko5YpZm8k7IduJg3ATTlOy1i0Nk3NQSg83RHi3TLuSNaXMnA8kzJ8DSr/2BmnK3ZU0UDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3c7ab15a05d94d24ef9f72a91850cbba65ddac0995f4e4aca2353702f6f2bcae","last_reissued_at":"2026-07-05T03:10:43.545215Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:10:43.545215Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2109.00179","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":"2026-07-05T03:10:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pUQ1kSM9hXBz3HTzruq/Z084Bon2be41jsFZktqqXnlG4AgjPOak52vtuJuzLPNGV62kYITlKh+j92qfsZQbBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T23:58:59.945452Z"},"content_sha256":"e2be5984c6bc4837288e86da764cf0c481f4dfcd08fb0af8980924e879561f86","schema_version":"1.0","event_id":"sha256:e2be5984c6bc4837288e86da764cf0c481f4dfcd08fb0af8980924e879561f86"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:HR5LCWQF3FGSJ347OKURQUGLXJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Siyuan Huang, Song-Chun Zhu, Yichen Xie, Yixin Zhu","submitted_at":"2021-09-01T04:17:11Z","abstract_excerpt":"To date, various 3D scene understanding tasks still lack practical and generalizable pre-trained models, primarily due to the intricate nature of 3D scene understanding tasks and their immense variations introduced by camera views, lighting, occlusions, etc. In this paper, we tackle this challenge by introducing a spatio-temporal representation learning (STRL) framework, capable of learning from unlabeled 3D point clouds in a self-supervised fashion. Inspired by how infants learn from visual data in the wild, we explore the rich spatio-temporal cues derived from the 3D data. Specifically, STRL"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.00179","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/2109.00179/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":"2026-07-05T03:10:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"F4/8jqWpArJmAZjh+uS7e9GiqLy3ubfgR+/GZZT+9xv4LXQXi2utWqkvNNwCqLeO2W5ulaY8Lj16KLR3nc7LCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T23:58:59.945986Z"},"content_sha256":"2d3dd92025d2f85ba9d83f948c43d99e153c32d3f8625563cdc67353f475f607","schema_version":"1.0","event_id":"sha256:2d3dd92025d2f85ba9d83f948c43d99e153c32d3f8625563cdc67353f475f607"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HR5LCWQF3FGSJ347OKURQUGLXJ/bundle.json","state_url":"https://pith.science/pith/HR5LCWQF3FGSJ347OKURQUGLXJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HR5LCWQF3FGSJ347OKURQUGLXJ/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":"2026-08-15T23:58:59Z","links":{"resolver":"https://pith.science/pith/HR5LCWQF3FGSJ347OKURQUGLXJ","bundle":"https://pith.science/pith/HR5LCWQF3FGSJ347OKURQUGLXJ/bundle.json","state":"https://pith.science/pith/HR5LCWQF3FGSJ347OKURQUGLXJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HR5LCWQF3FGSJ347OKURQUGLXJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:HR5LCWQF3FGSJ347OKURQUGLXJ","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":"8b63690c266baa93f04918a7bc778a68aca97c61b7a8b1b7895186dd0aa66024","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-09-01T04:17:11Z","title_canon_sha256":"905882fcefc9ac803c294fce4d0a717fb1277851e211d15dd030bc691ad2f94c"},"schema_version":"1.0","source":{"id":"2109.00179","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.00179","created_at":"2026-07-05T03:10:43Z"},{"alias_kind":"arxiv_version","alias_value":"2109.00179v1","created_at":"2026-07-05T03:10:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.00179","created_at":"2026-07-05T03:10:43Z"},{"alias_kind":"pith_short_12","alias_value":"HR5LCWQF3FGS","created_at":"2026-07-05T03:10:43Z"},{"alias_kind":"pith_short_16","alias_value":"HR5LCWQF3FGSJ347","created_at":"2026-07-05T03:10:43Z"},{"alias_kind":"pith_short_8","alias_value":"HR5LCWQF","created_at":"2026-07-05T03:10:43Z"}],"graph_snapshots":[{"event_id":"sha256:2d3dd92025d2f85ba9d83f948c43d99e153c32d3f8625563cdc67353f475f607","target":"graph","created_at":"2026-07-05T03:10:43Z","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/2109.00179/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"To date, various 3D scene understanding tasks still lack practical and generalizable pre-trained models, primarily due to the intricate nature of 3D scene understanding tasks and their immense variations introduced by camera views, lighting, occlusions, etc. In this paper, we tackle this challenge by introducing a spatio-temporal representation learning (STRL) framework, capable of learning from unlabeled 3D point clouds in a self-supervised fashion. Inspired by how infants learn from visual data in the wild, we explore the rich spatio-temporal cues derived from the 3D data. Specifically, STRL","authors_text":"Siyuan Huang, Song-Chun Zhu, Yichen Xie, Yixin Zhu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-09-01T04:17:11Z","title":"Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.00179","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:e2be5984c6bc4837288e86da764cf0c481f4dfcd08fb0af8980924e879561f86","target":"record","created_at":"2026-07-05T03:10:43Z","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":"8b63690c266baa93f04918a7bc778a68aca97c61b7a8b1b7895186dd0aa66024","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-09-01T04:17:11Z","title_canon_sha256":"905882fcefc9ac803c294fce4d0a717fb1277851e211d15dd030bc691ad2f94c"},"schema_version":"1.0","source":{"id":"2109.00179","kind":"arxiv","version":1}},"canonical_sha256":"3c7ab15a05d94d24ef9f72a91850cbba65ddac0995f4e4aca2353702f6f2bcae","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3c7ab15a05d94d24ef9f72a91850cbba65ddac0995f4e4aca2353702f6f2bcae","first_computed_at":"2026-07-05T03:10:43.545215Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:10:43.545215Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JWx8bHQb/6HT9TLkuko5YpZm8k7IduJg3ATTlOy1i0Nk3NQSg83RHi3TLuSNaXMnA8kzJ8DSr/2BmnK3ZU0UDw==","signature_status":"signed_v1","signed_at":"2026-07-05T03:10:43.545585Z","signed_message":"canonical_sha256_bytes"},"source_id":"2109.00179","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e2be5984c6bc4837288e86da764cf0c481f4dfcd08fb0af8980924e879561f86","sha256:2d3dd92025d2f85ba9d83f948c43d99e153c32d3f8625563cdc67353f475f607"],"state_sha256":"2ae91a2015a5cce9af658978e3a8145735b31269bdc662a864699f8a54a58432"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"z9sJ/bgp5g/yoSm69plcqPesNeE/zo7OxueCHLfzlIx9Uw72EwNIkhTB5UV0OdPTS4F5rzC+z1qOBGbK/oVkDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T23:58:59.949538Z","bundle_sha256":"ac8cae0d37bd3f8defda8c0fb447bb48637570a3c5dbba3428a1ad1b4f1efdee"}}