{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:LEIQVKIAUK2YNMEV3ZQOSRSIUY","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":"fe64e9a0af8bc852feaaea519f9ecb519a32d545deedf67ff228189894208423","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-25T04:20:04Z","title_canon_sha256":"0451dd259b180c1550b59a5a39de7a10cc4b4ca693dc6399b4983db88b1d4e51"},"schema_version":"1.0","source":{"id":"2507.18939","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.18939","created_at":"2026-07-05T11:43:16Z"},{"alias_kind":"arxiv_version","alias_value":"2507.18939v1","created_at":"2026-07-05T11:43:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.18939","created_at":"2026-07-05T11:43:16Z"},{"alias_kind":"pith_short_12","alias_value":"LEIQVKIAUK2Y","created_at":"2026-07-05T11:43:16Z"},{"alias_kind":"pith_short_16","alias_value":"LEIQVKIAUK2YNMEV","created_at":"2026-07-05T11:43:16Z"},{"alias_kind":"pith_short_8","alias_value":"LEIQVKIA","created_at":"2026-07-05T11:43:16Z"}],"graph_snapshots":[{"event_id":"sha256:9fe44554ae931aaee21d849e2edf3d1408265e7b74e954184004ab1f8bbce588","target":"graph","created_at":"2026-07-05T11:43:16Z","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/2507.18939/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Point-based representations have consistently played a vital role in geometric data structures. Most point cloud learning and processing methods typically leverage the unordered and unconstrained nature to represent the underlying geometry of 3D shapes. However, how to extract meaningful structural information from unstructured point cloud distributions and transform them into semantically meaningful point distributions remains an under-explored problem. We present PDT, a novel framework for point distribution transformation with diffusion models. Given a set of input points, PDT learns to tra","authors_text":"Cheng Lin, Hao-Xiang Guo, Jionghao Wang, Rui Xu, Taku Komura, Wenping Wang, Xiao-Xiao Long, Xin Li, Yuan Liu, Zhiyang Dou","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-25T04:20:04Z","title":"PDT: Point Distribution Transformation with Diffusion Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.18939","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:f51b82b73aa2e7f636213cf5b0875028344855f428dc4ec7aff3d74dddf5ec17","target":"record","created_at":"2026-07-05T11:43:16Z","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":"fe64e9a0af8bc852feaaea519f9ecb519a32d545deedf67ff228189894208423","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-07-25T04:20:04Z","title_canon_sha256":"0451dd259b180c1550b59a5a39de7a10cc4b4ca693dc6399b4983db88b1d4e51"},"schema_version":"1.0","source":{"id":"2507.18939","kind":"arxiv","version":1}},"canonical_sha256":"59110aa900a2b586b095de60e94648a602dbf75e18879016e8071f16edc0a3bd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"59110aa900a2b586b095de60e94648a602dbf75e18879016e8071f16edc0a3bd","first_computed_at":"2026-07-05T11:43:16.503245Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:43:16.503245Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"DrMzC81kHlVycw5ZopTHD4BnLBoRsvKsbN9/ulwnS3PJx3Haam8toqGX6DfrAPYLrSAQOF8KMUMZEHRT8x1gCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:43:16.503698Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.18939","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f51b82b73aa2e7f636213cf5b0875028344855f428dc4ec7aff3d74dddf5ec17","sha256:9fe44554ae931aaee21d849e2edf3d1408265e7b74e954184004ab1f8bbce588"],"state_sha256":"822c2898c9501afec1910254d2e65107d6df610edf82fdd32a5df3152063a5a7"}