{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:QWMTCP3LFV2GB25LOR3P4YZJYG","short_pith_number":"pith:QWMTCP3L","canonical_record":{"source":{"id":"2411.14158","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-11-21T14:19:32Z","cross_cats_sorted":[],"title_canon_sha256":"bb3acb7c725720ed27b1caebf2f23e867905d2c660b634e76c37ec9440c20148","abstract_canon_sha256":"a219b12333e718a0e4e44cfa66337c3da810b257b320437d537f5c940ec907ca"},"schema_version":"1.0"},"canonical_sha256":"8599313f6b2d7460ebab7476fe6329c1b815b32697c1d50cb31b014ae0a86dc1","source":{"kind":"arxiv","id":"2411.14158","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.14158","created_at":"2026-07-05T09:38:42Z"},{"alias_kind":"arxiv_version","alias_value":"2411.14158v1","created_at":"2026-07-05T09:38:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.14158","created_at":"2026-07-05T09:38:42Z"},{"alias_kind":"pith_short_12","alias_value":"QWMTCP3LFV2G","created_at":"2026-07-05T09:38:42Z"},{"alias_kind":"pith_short_16","alias_value":"QWMTCP3LFV2GB25L","created_at":"2026-07-05T09:38:42Z"},{"alias_kind":"pith_short_8","alias_value":"QWMTCP3L","created_at":"2026-07-05T09:38:42Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:QWMTCP3LFV2GB25LOR3P4YZJYG","target":"record","payload":{"canonical_record":{"source":{"id":"2411.14158","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-11-21T14:19:32Z","cross_cats_sorted":[],"title_canon_sha256":"bb3acb7c725720ed27b1caebf2f23e867905d2c660b634e76c37ec9440c20148","abstract_canon_sha256":"a219b12333e718a0e4e44cfa66337c3da810b257b320437d537f5c940ec907ca"},"schema_version":"1.0"},"canonical_sha256":"8599313f6b2d7460ebab7476fe6329c1b815b32697c1d50cb31b014ae0a86dc1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:38:42.525234Z","signature_b64":"aSDKovbt7WtbRMQjyKUJZc8nyIcqvtGKjKJhryDwp5mitj4ogOcBDNFGs+Qr2NXEwryPGe1jN9aRtFM144iDBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8599313f6b2d7460ebab7476fe6329c1b815b32697c1d50cb31b014ae0a86dc1","last_reissued_at":"2026-07-05T09:38:42.524763Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:38:42.524763Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2411.14158","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":"2026-07-05T09:38:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cf0689zKEs0eh+03HGhP6COGYq6jBNXaEnuhhi74mCMBQD8/YKt8eZPTETX4f1QVROjqKc8MA1T/QE9LBqswDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T22:26:41.592098Z"},"content_sha256":"887f5da7c5ef9d826285804297ec7fe34cb29f77de07317abbdb7b04d3baf9b9","schema_version":"1.0","event_id":"sha256:887f5da7c5ef9d826285804297ec7fe34cb29f77de07317abbdb7b04d3baf9b9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:QWMTCP3LFV2GB25LOR3P4YZJYG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Point Cloud Denoising With Fine-Granularity Dynamic Graph Convolutional Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chenglin Li, Duoduo Xue, Hongkai Xiong, Junni Zou, Wenqiang Xu, Wenrui Dai, Ziyang Zheng","submitted_at":"2024-11-21T14:19:32Z","abstract_excerpt":"Due to limitations in acquisition equipment, noise perturbations often corrupt 3-D point clouds, hindering down-stream tasks such as surface reconstruction, rendering, and further processing. Existing 3-D point cloud denoising methods typically fail to reliably fit the underlying continuous surface, resulting in a degradation of reconstruction performance. This paper introduces fine-granularity dynamic graph convolutional networks called GD-GCN, a novel approach to denoising in 3-D point clouds. The GD-GCN employs micro-step temporal graph convolution (MST-GConv) to perform feature learning in"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.14158","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/2411.14158/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":"2026-07-05T09:38:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"C7pXXXtFwdhDwoZIsJjD3RAvhiDlYChk91OSyBzwGqINIcy1oH8ioFOCmL4JDoQ0IiEaOhV7mXM443/S20MWAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T22:26:41.592606Z"},"content_sha256":"2f4c94e55b6d2aa8eb2c5767b5f0728410e685cc415451e786c417ae0ad6d149","schema_version":"1.0","event_id":"sha256:2f4c94e55b6d2aa8eb2c5767b5f0728410e685cc415451e786c417ae0ad6d149"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QWMTCP3LFV2GB25LOR3P4YZJYG/bundle.json","state_url":"https://pith.science/pith/QWMTCP3LFV2GB25LOR3P4YZJYG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QWMTCP3LFV2GB25LOR3P4YZJYG/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":"2026-08-08T22:26:41Z","links":{"resolver":"https://pith.science/pith/QWMTCP3LFV2GB25LOR3P4YZJYG","bundle":"https://pith.science/pith/QWMTCP3LFV2GB25LOR3P4YZJYG/bundle.json","state":"https://pith.science/pith/QWMTCP3LFV2GB25LOR3P4YZJYG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QWMTCP3LFV2GB25LOR3P4YZJYG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:QWMTCP3LFV2GB25LOR3P4YZJYG","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":"a219b12333e718a0e4e44cfa66337c3da810b257b320437d537f5c940ec907ca","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-11-21T14:19:32Z","title_canon_sha256":"bb3acb7c725720ed27b1caebf2f23e867905d2c660b634e76c37ec9440c20148"},"schema_version":"1.0","source":{"id":"2411.14158","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.14158","created_at":"2026-07-05T09:38:42Z"},{"alias_kind":"arxiv_version","alias_value":"2411.14158v1","created_at":"2026-07-05T09:38:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.14158","created_at":"2026-07-05T09:38:42Z"},{"alias_kind":"pith_short_12","alias_value":"QWMTCP3LFV2G","created_at":"2026-07-05T09:38:42Z"},{"alias_kind":"pith_short_16","alias_value":"QWMTCP3LFV2GB25L","created_at":"2026-07-05T09:38:42Z"},{"alias_kind":"pith_short_8","alias_value":"QWMTCP3L","created_at":"2026-07-05T09:38:42Z"}],"graph_snapshots":[{"event_id":"sha256:2f4c94e55b6d2aa8eb2c5767b5f0728410e685cc415451e786c417ae0ad6d149","target":"graph","created_at":"2026-07-05T09:38:42Z","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/2411.14158/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Due to limitations in acquisition equipment, noise perturbations often corrupt 3-D point clouds, hindering down-stream tasks such as surface reconstruction, rendering, and further processing. Existing 3-D point cloud denoising methods typically fail to reliably fit the underlying continuous surface, resulting in a degradation of reconstruction performance. This paper introduces fine-granularity dynamic graph convolutional networks called GD-GCN, a novel approach to denoising in 3-D point clouds. The GD-GCN employs micro-step temporal graph convolution (MST-GConv) to perform feature learning in","authors_text":"Chenglin Li, Duoduo Xue, Hongkai Xiong, Junni Zou, Wenqiang Xu, Wenrui Dai, Ziyang Zheng","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-11-21T14:19:32Z","title":"Point Cloud Denoising With Fine-Granularity Dynamic Graph Convolutional Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.14158","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":"sha256:887f5da7c5ef9d826285804297ec7fe34cb29f77de07317abbdb7b04d3baf9b9","target":"record","created_at":"2026-07-05T09:38:42Z","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":"a219b12333e718a0e4e44cfa66337c3da810b257b320437d537f5c940ec907ca","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-11-21T14:19:32Z","title_canon_sha256":"bb3acb7c725720ed27b1caebf2f23e867905d2c660b634e76c37ec9440c20148"},"schema_version":"1.0","source":{"id":"2411.14158","kind":"arxiv","version":1}},"canonical_sha256":"8599313f6b2d7460ebab7476fe6329c1b815b32697c1d50cb31b014ae0a86dc1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8599313f6b2d7460ebab7476fe6329c1b815b32697c1d50cb31b014ae0a86dc1","first_computed_at":"2026-07-05T09:38:42.524763Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:38:42.524763Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"aSDKovbt7WtbRMQjyKUJZc8nyIcqvtGKjKJhryDwp5mitj4ogOcBDNFGs+Qr2NXEwryPGe1jN9aRtFM144iDBA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:38:42.525234Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.14158","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:887f5da7c5ef9d826285804297ec7fe34cb29f77de07317abbdb7b04d3baf9b9","sha256:2f4c94e55b6d2aa8eb2c5767b5f0728410e685cc415451e786c417ae0ad6d149"],"state_sha256":"3396707543e9b1ff0e53f249854f51a2f5fae45ae1b56d3d71713a1232816014"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5Ws5+hTGvLOKTvf4YHxbja44CFM38AD8IxQ2MaL/zPWC4kt8mXOWLLgr3EvXbFYEQsnSE3fV+59IY6AjHl38DQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T22:26:41.597586Z","bundle_sha256":"a20f98204808119832bb9269829b768838a5982e6798d11438d9584c019ea1a8"}}