{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:V6UGSNULOTGHJLUMWMKW7BZX6P","short_pith_number":"pith:V6UGSNUL","canonical_record":{"source":{"id":"2506.09952","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-11T17:23:21Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"9b3aa815b21cdd6c8c65be9f946e590ce61a573281bf2fb746ec94c98ba7f857","abstract_canon_sha256":"42c184b8cf0cc4a317e01a938e3f5dbbebd3193ec26c9be34cbdb2922745c4ba"},"schema_version":"1.0"},"canonical_sha256":"afa869368b74cc74ae8cb3156f8737f3e06068abecd19eb63632474eacf1a1da","source":{"kind":"arxiv","id":"2506.09952","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.09952","created_at":"2026-07-05T11:19:59Z"},{"alias_kind":"arxiv_version","alias_value":"2506.09952v1","created_at":"2026-07-05T11:19:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.09952","created_at":"2026-07-05T11:19:59Z"},{"alias_kind":"pith_short_12","alias_value":"V6UGSNULOTGH","created_at":"2026-07-05T11:19:59Z"},{"alias_kind":"pith_short_16","alias_value":"V6UGSNULOTGHJLUM","created_at":"2026-07-05T11:19:59Z"},{"alias_kind":"pith_short_8","alias_value":"V6UGSNUL","created_at":"2026-07-05T11:19:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:V6UGSNULOTGHJLUMWMKW7BZX6P","target":"record","payload":{"canonical_record":{"source":{"id":"2506.09952","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-11T17:23:21Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"9b3aa815b21cdd6c8c65be9f946e590ce61a573281bf2fb746ec94c98ba7f857","abstract_canon_sha256":"42c184b8cf0cc4a317e01a938e3f5dbbebd3193ec26c9be34cbdb2922745c4ba"},"schema_version":"1.0"},"canonical_sha256":"afa869368b74cc74ae8cb3156f8737f3e06068abecd19eb63632474eacf1a1da","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:19:59.461838Z","signature_b64":"6Oa0k6pNW/oq1YZCJjnmafR2BXrLiYJy9a6IZ3zWD9m70eTo3gxX857Jgb36qz94bmsiTja0RlxYW4XBG0YYCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"afa869368b74cc74ae8cb3156f8737f3e06068abecd19eb63632474eacf1a1da","last_reissued_at":"2026-07-05T11:19:59.461354Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:19:59.461354Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.09952","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":"2026-07-05T11:19:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DCXEkIeUBxJKiuOOWa6mcdhs0KUyZlJtBDTL2UZYvnpc4Jax+PTkXPZR75zFmmczV1sqQOacqwJ35VsCaCHOCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T12:42:22.130022Z"},"content_sha256":"65bccd1d8a6eb7e44a9448422708d311dbadfb8586140cf0cf1b67edf9836483","schema_version":"1.0","event_id":"sha256:65bccd1d8a6eb7e44a9448422708d311dbadfb8586140cf0cf1b67edf9836483"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:V6UGSNULOTGHJLUMWMKW7BZX6P","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Jie Zhou, Jiwen Lu, Yanran Zhang, Ziyi Wang","submitted_at":"2025-06-11T17:23:21Z","abstract_excerpt":"The scale diversity of point cloud data presents significant challenges in developing unified representation learning techniques for 3D vision. Currently, there are few unified 3D models, and no existing pre-training method is equally effective for both object- and scene-level point clouds. In this paper, we introduce UniPre3D, the first unified pre-training method that can be seamlessly applied to point clouds of any scale and 3D models of any architecture. Our approach predicts Gaussian primitives as the pre-training task and employs differentiable Gaussian splatting to render images, enabli"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.09952","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/2506.09952/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":"2026-07-05T11:19:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fDvxDSWTj+FY0AdiE9onC2sMYJdYDsA6E6AdeXp46rkogWTTAyATjsqJbaeMijZZEZCfdoP7ypNeYmxKYd+1Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T12:42:22.130517Z"},"content_sha256":"b5cca829ee2172b4dd240615f1326e5271e9d2e361763091ef2a4ff8ee38fd8a","schema_version":"1.0","event_id":"sha256:b5cca829ee2172b4dd240615f1326e5271e9d2e361763091ef2a4ff8ee38fd8a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/V6UGSNULOTGHJLUMWMKW7BZX6P/bundle.json","state_url":"https://pith.science/pith/V6UGSNULOTGHJLUMWMKW7BZX6P/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/V6UGSNULOTGHJLUMWMKW7BZX6P/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":"2026-08-07T12:42:22Z","links":{"resolver":"https://pith.science/pith/V6UGSNULOTGHJLUMWMKW7BZX6P","bundle":"https://pith.science/pith/V6UGSNULOTGHJLUMWMKW7BZX6P/bundle.json","state":"https://pith.science/pith/V6UGSNULOTGHJLUMWMKW7BZX6P/state.json","well_known_bundle":"https://pith.science/.well-known/pith/V6UGSNULOTGHJLUMWMKW7BZX6P/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:V6UGSNULOTGHJLUMWMKW7BZX6P","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":"42c184b8cf0cc4a317e01a938e3f5dbbebd3193ec26c9be34cbdb2922745c4ba","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-11T17:23:21Z","title_canon_sha256":"9b3aa815b21cdd6c8c65be9f946e590ce61a573281bf2fb746ec94c98ba7f857"},"schema_version":"1.0","source":{"id":"2506.09952","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.09952","created_at":"2026-07-05T11:19:59Z"},{"alias_kind":"arxiv_version","alias_value":"2506.09952v1","created_at":"2026-07-05T11:19:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.09952","created_at":"2026-07-05T11:19:59Z"},{"alias_kind":"pith_short_12","alias_value":"V6UGSNULOTGH","created_at":"2026-07-05T11:19:59Z"},{"alias_kind":"pith_short_16","alias_value":"V6UGSNULOTGHJLUM","created_at":"2026-07-05T11:19:59Z"},{"alias_kind":"pith_short_8","alias_value":"V6UGSNUL","created_at":"2026-07-05T11:19:59Z"}],"graph_snapshots":[{"event_id":"sha256:b5cca829ee2172b4dd240615f1326e5271e9d2e361763091ef2a4ff8ee38fd8a","target":"graph","created_at":"2026-07-05T11:19:59Z","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/2506.09952/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The scale diversity of point cloud data presents significant challenges in developing unified representation learning techniques for 3D vision. Currently, there are few unified 3D models, and no existing pre-training method is equally effective for both object- and scene-level point clouds. In this paper, we introduce UniPre3D, the first unified pre-training method that can be seamlessly applied to point clouds of any scale and 3D models of any architecture. Our approach predicts Gaussian primitives as the pre-training task and employs differentiable Gaussian splatting to render images, enabli","authors_text":"Jie Zhou, Jiwen Lu, Yanran Zhang, Ziyi Wang","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-11T17:23:21Z","title":"UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.09952","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":"sha256:65bccd1d8a6eb7e44a9448422708d311dbadfb8586140cf0cf1b67edf9836483","target":"record","created_at":"2026-07-05T11:19:59Z","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":"42c184b8cf0cc4a317e01a938e3f5dbbebd3193ec26c9be34cbdb2922745c4ba","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-06-11T17:23:21Z","title_canon_sha256":"9b3aa815b21cdd6c8c65be9f946e590ce61a573281bf2fb746ec94c98ba7f857"},"schema_version":"1.0","source":{"id":"2506.09952","kind":"arxiv","version":1}},"canonical_sha256":"afa869368b74cc74ae8cb3156f8737f3e06068abecd19eb63632474eacf1a1da","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"afa869368b74cc74ae8cb3156f8737f3e06068abecd19eb63632474eacf1a1da","first_computed_at":"2026-07-05T11:19:59.461354Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:19:59.461354Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6Oa0k6pNW/oq1YZCJjnmafR2BXrLiYJy9a6IZ3zWD9m70eTo3gxX857Jgb36qz94bmsiTja0RlxYW4XBG0YYCw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:19:59.461838Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.09952","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:65bccd1d8a6eb7e44a9448422708d311dbadfb8586140cf0cf1b67edf9836483","sha256:b5cca829ee2172b4dd240615f1326e5271e9d2e361763091ef2a4ff8ee38fd8a"],"state_sha256":"ed8e5c9b6b5514981853242c78923145fbcd2b43f27b67ba80db72c5ffbda693"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ao4uIRm63DKpgJ1uGB7qvHVVA7aUW4s9KPHffGCtzE9IqEOy2X7jNqBAlxFzRmS3kC1UqNQ4yI5cnB8DB4I6CA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T12:42:22.135965Z","bundle_sha256":"7c39a07ae5c7ba70ae0abe3c13cba29efaf06d449ba301ce30ff652629c08f38"}}