{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:GMIYSCS5S3XLSNX72EJWRWKEG4","short_pith_number":"pith:GMIYSCS5","canonical_record":{"source":{"id":"2411.18667","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-11-27T16:11:45Z","cross_cats_sorted":[],"title_canon_sha256":"1e44b2a543a9e9a0a65672d76b171667a5d471b0abb3297b191157c1e77c86c4","abstract_canon_sha256":"e4a9bf620b0efd793b24c8676cba49e812034ef72770e5ee053c5ab228bd1198"},"schema_version":"1.0"},"canonical_sha256":"3311890a5d96eeb936ffd11368d944373c8b48e10b73a45e68e5d52fd9932c11","source":{"kind":"arxiv","id":"2411.18667","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.18667","created_at":"2026-07-05T09:41:49Z"},{"alias_kind":"arxiv_version","alias_value":"2411.18667v1","created_at":"2026-07-05T09:41:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.18667","created_at":"2026-07-05T09:41:49Z"},{"alias_kind":"pith_short_12","alias_value":"GMIYSCS5S3XL","created_at":"2026-07-05T09:41:49Z"},{"alias_kind":"pith_short_16","alias_value":"GMIYSCS5S3XLSNX7","created_at":"2026-07-05T09:41:49Z"},{"alias_kind":"pith_short_8","alias_value":"GMIYSCS5","created_at":"2026-07-05T09:41:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:GMIYSCS5S3XLSNX72EJWRWKEG4","target":"record","payload":{"canonical_record":{"source":{"id":"2411.18667","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-11-27T16:11:45Z","cross_cats_sorted":[],"title_canon_sha256":"1e44b2a543a9e9a0a65672d76b171667a5d471b0abb3297b191157c1e77c86c4","abstract_canon_sha256":"e4a9bf620b0efd793b24c8676cba49e812034ef72770e5ee053c5ab228bd1198"},"schema_version":"1.0"},"canonical_sha256":"3311890a5d96eeb936ffd11368d944373c8b48e10b73a45e68e5d52fd9932c11","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:41:49.170286Z","signature_b64":"14q9PkpM/v2w0QojlHJucY8OtqDV3PUY8n5+tI3IN4RmhrkzKbjjamrbRvhha99XUEheNK9/KhRcWyUrQcGjDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3311890a5d96eeb936ffd11368d944373c8b48e10b73a45e68e5d52fd9932c11","last_reissued_at":"2026-07-05T09:41:49.169809Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:41:49.169809Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2411.18667","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":"2026-07-05T09:41:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RM6V/NPdL5SO1ZRC9UesHpezip8YxXFFrb78aZXUXPHWt0XWU+hKstlcs+fJB1kbvbH3Py7b93biIG1pkGAfBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-31T19:44:36.265610Z"},"content_sha256":"b919fb9013bc6141e497cae97e43ce93e8f332c930ea616370b4df6f703989ab","schema_version":"1.0","event_id":"sha256:b919fb9013bc6141e497cae97e43ce93e8f332c930ea616370b4df6f703989ab"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:GMIYSCS5S3XLSNX72EJWRWKEG4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Point Cloud Unsupervised Pre-training via 3D Gaussian Splatting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Haihong Xiao, Hao Liu, Minglin Chen, Yanni Ma, Ying He","submitted_at":"2024-11-27T16:11:45Z","abstract_excerpt":"Pre-training on large-scale unlabeled datasets contribute to the model achieving powerful performance on 3D vision tasks, especially when annotations are limited. However, existing rendering-based self-supervised frameworks are computationally demanding and memory-intensive during pre-training due to the inherent nature of volume rendering. In this paper, we propose an efficient framework named GS$^3$ to learn point cloud representation, which seamlessly integrates fast 3D Gaussian Splatting into the rendering-based framework. The core idea behind our framework is to pre-train the point cloud "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.18667","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/2411.18667/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":"2026-07-05T09:41:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Boqv7fvKLcTCduVzvhdFS4QxqiEdc1EELZ/NT4NlokfxeKD6XUAxr3f+nqIT2mPxCEyBjobrGQjcAs234NOBCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-31T19:44:36.266086Z"},"content_sha256":"7569cdf08a18a8e365abce67ffb192e111896372c7933e1abe05f4d8d932d768","schema_version":"1.0","event_id":"sha256:7569cdf08a18a8e365abce67ffb192e111896372c7933e1abe05f4d8d932d768"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GMIYSCS5S3XLSNX72EJWRWKEG4/bundle.json","state_url":"https://pith.science/pith/GMIYSCS5S3XLSNX72EJWRWKEG4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GMIYSCS5S3XLSNX72EJWRWKEG4/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":"2026-07-31T19:44:36Z","links":{"resolver":"https://pith.science/pith/GMIYSCS5S3XLSNX72EJWRWKEG4","bundle":"https://pith.science/pith/GMIYSCS5S3XLSNX72EJWRWKEG4/bundle.json","state":"https://pith.science/pith/GMIYSCS5S3XLSNX72EJWRWKEG4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GMIYSCS5S3XLSNX72EJWRWKEG4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:GMIYSCS5S3XLSNX72EJWRWKEG4","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":"e4a9bf620b0efd793b24c8676cba49e812034ef72770e5ee053c5ab228bd1198","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-11-27T16:11:45Z","title_canon_sha256":"1e44b2a543a9e9a0a65672d76b171667a5d471b0abb3297b191157c1e77c86c4"},"schema_version":"1.0","source":{"id":"2411.18667","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.18667","created_at":"2026-07-05T09:41:49Z"},{"alias_kind":"arxiv_version","alias_value":"2411.18667v1","created_at":"2026-07-05T09:41:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.18667","created_at":"2026-07-05T09:41:49Z"},{"alias_kind":"pith_short_12","alias_value":"GMIYSCS5S3XL","created_at":"2026-07-05T09:41:49Z"},{"alias_kind":"pith_short_16","alias_value":"GMIYSCS5S3XLSNX7","created_at":"2026-07-05T09:41:49Z"},{"alias_kind":"pith_short_8","alias_value":"GMIYSCS5","created_at":"2026-07-05T09:41:49Z"}],"graph_snapshots":[{"event_id":"sha256:7569cdf08a18a8e365abce67ffb192e111896372c7933e1abe05f4d8d932d768","target":"graph","created_at":"2026-07-05T09:41:49Z","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/2411.18667/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Pre-training on large-scale unlabeled datasets contribute to the model achieving powerful performance on 3D vision tasks, especially when annotations are limited. However, existing rendering-based self-supervised frameworks are computationally demanding and memory-intensive during pre-training due to the inherent nature of volume rendering. In this paper, we propose an efficient framework named GS$^3$ to learn point cloud representation, which seamlessly integrates fast 3D Gaussian Splatting into the rendering-based framework. The core idea behind our framework is to pre-train the point cloud ","authors_text":"Haihong Xiao, Hao Liu, Minglin Chen, Yanni Ma, Ying He","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-11-27T16:11:45Z","title":"Point Cloud Unsupervised Pre-training via 3D Gaussian Splatting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.18667","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:b919fb9013bc6141e497cae97e43ce93e8f332c930ea616370b4df6f703989ab","target":"record","created_at":"2026-07-05T09:41:49Z","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":"e4a9bf620b0efd793b24c8676cba49e812034ef72770e5ee053c5ab228bd1198","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-11-27T16:11:45Z","title_canon_sha256":"1e44b2a543a9e9a0a65672d76b171667a5d471b0abb3297b191157c1e77c86c4"},"schema_version":"1.0","source":{"id":"2411.18667","kind":"arxiv","version":1}},"canonical_sha256":"3311890a5d96eeb936ffd11368d944373c8b48e10b73a45e68e5d52fd9932c11","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3311890a5d96eeb936ffd11368d944373c8b48e10b73a45e68e5d52fd9932c11","first_computed_at":"2026-07-05T09:41:49.169809Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:41:49.169809Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"14q9PkpM/v2w0QojlHJucY8OtqDV3PUY8n5+tI3IN4RmhrkzKbjjamrbRvhha99XUEheNK9/KhRcWyUrQcGjDw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:41:49.170286Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.18667","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b919fb9013bc6141e497cae97e43ce93e8f332c930ea616370b4df6f703989ab","sha256:7569cdf08a18a8e365abce67ffb192e111896372c7933e1abe05f4d8d932d768"],"state_sha256":"9e67997e08af1a67572953d882f518430fa2a833c0ba958295dd9dd89b155c4c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"L+TfAp8b72lVisHrQwj8o43xKzxHIRcrlXJA9f2sl3xSMCIE+75ya8ezfDF/McYxxRU8lVI8PC5P/YMxvl5CCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-31T19:44:36.273092Z","bundle_sha256":"04d426e57cba0511363ddec04dd05a22c5e5280f0b9c7a7a242472f2e652aba2"}}