{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:CDKEP5FG7VYL4II77PHD37NJAY","short_pith_number":"pith:CDKEP5FG","canonical_record":{"source":{"id":"2406.02263","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-06-04T12:33:02Z","cross_cats_sorted":[],"title_canon_sha256":"a8039826bd12c06481346f2b27251dc0553f21c6157069c2f989d0d78d3502b0","abstract_canon_sha256":"be89702f2ab9357201c3eae6f12e610edf70adbac4c867a84371557315dd4010"},"schema_version":"1.0"},"canonical_sha256":"10d447f4a6fd70be211ffbce3dfda90609f3f17218d9a3794d10f53138c44a83","source":{"kind":"arxiv","id":"2406.02263","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.02263","created_at":"2026-07-05T08:27:17Z"},{"alias_kind":"arxiv_version","alias_value":"2406.02263v1","created_at":"2026-07-05T08:27:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.02263","created_at":"2026-07-05T08:27:17Z"},{"alias_kind":"pith_short_12","alias_value":"CDKEP5FG7VYL","created_at":"2026-07-05T08:27:17Z"},{"alias_kind":"pith_short_16","alias_value":"CDKEP5FG7VYL4II7","created_at":"2026-07-05T08:27:17Z"},{"alias_kind":"pith_short_8","alias_value":"CDKEP5FG","created_at":"2026-07-05T08:27:17Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:CDKEP5FG7VYL4II77PHD37NJAY","target":"record","payload":{"canonical_record":{"source":{"id":"2406.02263","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-06-04T12:33:02Z","cross_cats_sorted":[],"title_canon_sha256":"a8039826bd12c06481346f2b27251dc0553f21c6157069c2f989d0d78d3502b0","abstract_canon_sha256":"be89702f2ab9357201c3eae6f12e610edf70adbac4c867a84371557315dd4010"},"schema_version":"1.0"},"canonical_sha256":"10d447f4a6fd70be211ffbce3dfda90609f3f17218d9a3794d10f53138c44a83","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:27:17.132275Z","signature_b64":"k822VtW2occzo6VjE1WTmvnOB8+M2e7zP1E5uhmknOIONjBfaOjawOrv5D04STbl+qfqBAiQjJc4ofSuFJzTAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"10d447f4a6fd70be211ffbce3dfda90609f3f17218d9a3794d10f53138c44a83","last_reissued_at":"2026-07-05T08:27:17.131790Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:27:17.131790Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2406.02263","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":"2026-07-05T08:27:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FHCpbPfTnfs1VmXjo1mAzu4HGFWCpN5uZ1ptql/iKl/93gN3UQw6eMDu9iEEDX3AcO8P2dXm3HeK4wFtjaYCCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T22:35:58.961100Z"},"content_sha256":"b6de766f6203ff0e124aebe8a4f74d02364b7ebb031a814ea927e2d00b9b250a","schema_version":"1.0","event_id":"sha256:b6de766f6203ff0e124aebe8a4f74d02364b7ebb031a814ea927e2d00b9b250a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:CDKEP5FG7VYL4II77PHD37NJAY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoising","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chengjie Wang, Haokun Zhu, Jiangning Zhang, Jinlong Peng, Lizhuang Ma, Ran Yi, Yue Wang, Yunsheng Wu","submitted_at":"2024-06-04T12:33:02Z","abstract_excerpt":"Existing industrial anomaly detection methods primarily concentrate on unsupervised learning with pristine RGB images. Yet, both RGB and 3D data are crucial for anomaly detection, and the datasets are seldom completely clean in practical scenarios. To address above challenges, this paper initially delves into the RGB-3D multi-modal noisy anomaly detection, proposing a novel noise-resistant M3DM-NR framework to leveraging strong multi-modal discriminative capabilities of CLIP. M3DM-NR consists of three stages: Stage-I introduces the Suspected References Selection module to filter a few normal s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.02263","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/2406.02263/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":"2026-07-05T08:27:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pxmMifw0DnbdCH3SsuSta7dAPN9gTJUVnoaO9D4VOZMHbw4aozVm7lgSKuLSbC/3FOtpTmRBDQeI+frrZgL+DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T22:35:58.961716Z"},"content_sha256":"13fef1d2e7259bc76e07e7f77427450568c7993d3104a71cb87bec2f9a49b9ce","schema_version":"1.0","event_id":"sha256:13fef1d2e7259bc76e07e7f77427450568c7993d3104a71cb87bec2f9a49b9ce"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CDKEP5FG7VYL4II77PHD37NJAY/bundle.json","state_url":"https://pith.science/pith/CDKEP5FG7VYL4II77PHD37NJAY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CDKEP5FG7VYL4II77PHD37NJAY/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":"2026-08-12T22:35:58Z","links":{"resolver":"https://pith.science/pith/CDKEP5FG7VYL4II77PHD37NJAY","bundle":"https://pith.science/pith/CDKEP5FG7VYL4II77PHD37NJAY/bundle.json","state":"https://pith.science/pith/CDKEP5FG7VYL4II77PHD37NJAY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CDKEP5FG7VYL4II77PHD37NJAY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:CDKEP5FG7VYL4II77PHD37NJAY","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":"be89702f2ab9357201c3eae6f12e610edf70adbac4c867a84371557315dd4010","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-06-04T12:33:02Z","title_canon_sha256":"a8039826bd12c06481346f2b27251dc0553f21c6157069c2f989d0d78d3502b0"},"schema_version":"1.0","source":{"id":"2406.02263","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.02263","created_at":"2026-07-05T08:27:17Z"},{"alias_kind":"arxiv_version","alias_value":"2406.02263v1","created_at":"2026-07-05T08:27:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.02263","created_at":"2026-07-05T08:27:17Z"},{"alias_kind":"pith_short_12","alias_value":"CDKEP5FG7VYL","created_at":"2026-07-05T08:27:17Z"},{"alias_kind":"pith_short_16","alias_value":"CDKEP5FG7VYL4II7","created_at":"2026-07-05T08:27:17Z"},{"alias_kind":"pith_short_8","alias_value":"CDKEP5FG","created_at":"2026-07-05T08:27:17Z"}],"graph_snapshots":[{"event_id":"sha256:13fef1d2e7259bc76e07e7f77427450568c7993d3104a71cb87bec2f9a49b9ce","target":"graph","created_at":"2026-07-05T08:27:17Z","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/2406.02263/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Existing industrial anomaly detection methods primarily concentrate on unsupervised learning with pristine RGB images. Yet, both RGB and 3D data are crucial for anomaly detection, and the datasets are seldom completely clean in practical scenarios. To address above challenges, this paper initially delves into the RGB-3D multi-modal noisy anomaly detection, proposing a novel noise-resistant M3DM-NR framework to leveraging strong multi-modal discriminative capabilities of CLIP. M3DM-NR consists of three stages: Stage-I introduces the Suspected References Selection module to filter a few normal s","authors_text":"Chengjie Wang, Haokun Zhu, Jiangning Zhang, Jinlong Peng, Lizhuang Ma, Ran Yi, Yue Wang, Yunsheng Wu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-06-04T12:33:02Z","title":"M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoising"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.02263","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:b6de766f6203ff0e124aebe8a4f74d02364b7ebb031a814ea927e2d00b9b250a","target":"record","created_at":"2026-07-05T08:27:17Z","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":"be89702f2ab9357201c3eae6f12e610edf70adbac4c867a84371557315dd4010","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-06-04T12:33:02Z","title_canon_sha256":"a8039826bd12c06481346f2b27251dc0553f21c6157069c2f989d0d78d3502b0"},"schema_version":"1.0","source":{"id":"2406.02263","kind":"arxiv","version":1}},"canonical_sha256":"10d447f4a6fd70be211ffbce3dfda90609f3f17218d9a3794d10f53138c44a83","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"10d447f4a6fd70be211ffbce3dfda90609f3f17218d9a3794d10f53138c44a83","first_computed_at":"2026-07-05T08:27:17.131790Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:27:17.131790Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"k822VtW2occzo6VjE1WTmvnOB8+M2e7zP1E5uhmknOIONjBfaOjawOrv5D04STbl+qfqBAiQjJc4ofSuFJzTAw==","signature_status":"signed_v1","signed_at":"2026-07-05T08:27:17.132275Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.02263","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b6de766f6203ff0e124aebe8a4f74d02364b7ebb031a814ea927e2d00b9b250a","sha256:13fef1d2e7259bc76e07e7f77427450568c7993d3104a71cb87bec2f9a49b9ce"],"state_sha256":"631514d11d8cf40ae202247c0b2763acdbed56bf1919c4fe73c20b33225d0829"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Hb3/Mn+e0rkbkChWElW3XG2GAzcRwJ8KKk/Q9n2hZfieuczAWLgPlRG9vTf5P8Bj3hBAyUcIpa67mVl0tU2mAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-12T22:35:58.965865Z","bundle_sha256":"50cacd2a175e57e5fb75daa5d7df61b56d821ec0e4256a6a3a33fb9e3de121fb"}}