{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:FJQ4KEUXLRETJEEDE6IKPS6KLR","short_pith_number":"pith:FJQ4KEUX","canonical_record":{"source":{"id":"2201.04833","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-01-13T08:33:53Z","cross_cats_sorted":[],"title_canon_sha256":"dbc5cd052e468b1a67b4571eaa8404287779ad08e2ecf59945ad0e4d41833eff","abstract_canon_sha256":"893d8c0b52834c706c1c39e3b63fdfdc5e5ce5e32fe5d73bd492669d014eadef"},"schema_version":"1.0"},"canonical_sha256":"2a61c512975c493490832790a7cbca5c4a041f2793adec8e0469210352a85b80","source":{"kind":"arxiv","id":"2201.04833","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2201.04833","created_at":"2026-07-05T03:48:13Z"},{"alias_kind":"arxiv_version","alias_value":"2201.04833v1","created_at":"2026-07-05T03:48:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.04833","created_at":"2026-07-05T03:48:13Z"},{"alias_kind":"pith_short_12","alias_value":"FJQ4KEUXLRET","created_at":"2026-07-05T03:48:13Z"},{"alias_kind":"pith_short_16","alias_value":"FJQ4KEUXLRETJEED","created_at":"2026-07-05T03:48:13Z"},{"alias_kind":"pith_short_8","alias_value":"FJQ4KEUX","created_at":"2026-07-05T03:48:13Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:FJQ4KEUXLRETJEEDE6IKPS6KLR","target":"record","payload":{"canonical_record":{"source":{"id":"2201.04833","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-01-13T08:33:53Z","cross_cats_sorted":[],"title_canon_sha256":"dbc5cd052e468b1a67b4571eaa8404287779ad08e2ecf59945ad0e4d41833eff","abstract_canon_sha256":"893d8c0b52834c706c1c39e3b63fdfdc5e5ce5e32fe5d73bd492669d014eadef"},"schema_version":"1.0"},"canonical_sha256":"2a61c512975c493490832790a7cbca5c4a041f2793adec8e0469210352a85b80","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:48:13.329194Z","signature_b64":"rCdK3D17kOycv92eDnzEpZWM6X8fnKffdpyBt2UY1zu9/H3FistUySYoTJNcjS+nUB8Zsm4Sk/LphGWvjNrcDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2a61c512975c493490832790a7cbca5c4a041f2793adec8e0469210352a85b80","last_reissued_at":"2026-07-05T03:48:13.328770Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:48:13.328770Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2201.04833","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":"2026-07-05T03:48:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"u2T2xd3hVdJT7YfWO7alq5rAA/0zD6xR94cDv2gAzsF1SpI8OQoNc6Mwx1BmXbupvML9nUmK/ZwkOWk2uj10Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-31T20:21:47.043680Z"},"content_sha256":"20fbea452c2b26eaaa5652bade48f08766b97c505727d1a7be51a592e3d72dca","schema_version":"1.0","event_id":"sha256:20fbea452c2b26eaaa5652bade48f08766b97c505727d1a7be51a592e3d72dca"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:FJQ4KEUXLRETJEEDE6IKPS6KLR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SnapshotNet: Self-supervised Feature Learning for Point Cloud Data Segmentation Using Minimal Labeled Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ling Zhang, Xingye Li, Zhigang Zhu","submitted_at":"2022-01-13T08:33:53Z","abstract_excerpt":"Manually annotating complex scene point cloud datasets is both costly and error-prone. To reduce the reliance on labeled data, a new model called SnapshotNet is proposed as a self-supervised feature learning approach, which directly works on the unlabeled point cloud data of a complex 3D scene. The SnapshotNet pipeline includes three stages. In the snapshot capturing stage, snapshots, which are defined as local collections of points, are sampled from the point cloud scene. A snapshot could be a view of a local 3D scan directly captured from the real scene, or a virtual view of such from a larg"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.04833","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/2201.04833/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":"2026-07-05T03:48:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SXygJDb+Do/x9C27zSx7Is/nKHRhgB1yMqviciaRZbqaS/hUHG7PGrT46E9kNVJwufR7ln/cfMYP2ex/6viaBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-31T20:21:47.044400Z"},"content_sha256":"6044fb7579b66e08bac1820d48bc2b6bf8452679f185a4849acea26a01b2b22d","schema_version":"1.0","event_id":"sha256:6044fb7579b66e08bac1820d48bc2b6bf8452679f185a4849acea26a01b2b22d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FJQ4KEUXLRETJEEDE6IKPS6KLR/bundle.json","state_url":"https://pith.science/pith/FJQ4KEUXLRETJEEDE6IKPS6KLR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FJQ4KEUXLRETJEEDE6IKPS6KLR/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":"2026-07-31T20:21:47Z","links":{"resolver":"https://pith.science/pith/FJQ4KEUXLRETJEEDE6IKPS6KLR","bundle":"https://pith.science/pith/FJQ4KEUXLRETJEEDE6IKPS6KLR/bundle.json","state":"https://pith.science/pith/FJQ4KEUXLRETJEEDE6IKPS6KLR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FJQ4KEUXLRETJEEDE6IKPS6KLR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:FJQ4KEUXLRETJEEDE6IKPS6KLR","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":"893d8c0b52834c706c1c39e3b63fdfdc5e5ce5e32fe5d73bd492669d014eadef","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-01-13T08:33:53Z","title_canon_sha256":"dbc5cd052e468b1a67b4571eaa8404287779ad08e2ecf59945ad0e4d41833eff"},"schema_version":"1.0","source":{"id":"2201.04833","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2201.04833","created_at":"2026-07-05T03:48:13Z"},{"alias_kind":"arxiv_version","alias_value":"2201.04833v1","created_at":"2026-07-05T03:48:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.04833","created_at":"2026-07-05T03:48:13Z"},{"alias_kind":"pith_short_12","alias_value":"FJQ4KEUXLRET","created_at":"2026-07-05T03:48:13Z"},{"alias_kind":"pith_short_16","alias_value":"FJQ4KEUXLRETJEED","created_at":"2026-07-05T03:48:13Z"},{"alias_kind":"pith_short_8","alias_value":"FJQ4KEUX","created_at":"2026-07-05T03:48:13Z"}],"graph_snapshots":[{"event_id":"sha256:6044fb7579b66e08bac1820d48bc2b6bf8452679f185a4849acea26a01b2b22d","target":"graph","created_at":"2026-07-05T03:48:13Z","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/2201.04833/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Manually annotating complex scene point cloud datasets is both costly and error-prone. To reduce the reliance on labeled data, a new model called SnapshotNet is proposed as a self-supervised feature learning approach, which directly works on the unlabeled point cloud data of a complex 3D scene. The SnapshotNet pipeline includes three stages. In the snapshot capturing stage, snapshots, which are defined as local collections of points, are sampled from the point cloud scene. A snapshot could be a view of a local 3D scan directly captured from the real scene, or a virtual view of such from a larg","authors_text":"Ling Zhang, Xingye Li, Zhigang Zhu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-01-13T08:33:53Z","title":"SnapshotNet: Self-supervised Feature Learning for Point Cloud Data Segmentation Using Minimal Labeled Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.04833","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":"sha256:20fbea452c2b26eaaa5652bade48f08766b97c505727d1a7be51a592e3d72dca","target":"record","created_at":"2026-07-05T03:48:13Z","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":"893d8c0b52834c706c1c39e3b63fdfdc5e5ce5e32fe5d73bd492669d014eadef","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-01-13T08:33:53Z","title_canon_sha256":"dbc5cd052e468b1a67b4571eaa8404287779ad08e2ecf59945ad0e4d41833eff"},"schema_version":"1.0","source":{"id":"2201.04833","kind":"arxiv","version":1}},"canonical_sha256":"2a61c512975c493490832790a7cbca5c4a041f2793adec8e0469210352a85b80","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2a61c512975c493490832790a7cbca5c4a041f2793adec8e0469210352a85b80","first_computed_at":"2026-07-05T03:48:13.328770Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:48:13.328770Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rCdK3D17kOycv92eDnzEpZWM6X8fnKffdpyBt2UY1zu9/H3FistUySYoTJNcjS+nUB8Zsm4Sk/LphGWvjNrcDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T03:48:13.329194Z","signed_message":"canonical_sha256_bytes"},"source_id":"2201.04833","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:20fbea452c2b26eaaa5652bade48f08766b97c505727d1a7be51a592e3d72dca","sha256:6044fb7579b66e08bac1820d48bc2b6bf8452679f185a4849acea26a01b2b22d"],"state_sha256":"c49c375c4be9d7fc84520502b28e9801346b3e6d587c6815802a32147bde84cb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"c245gHCmJQq81dEI9GIYslLwvzXlOcg6RnKME8Ql1j2Pn5KrLFbFHXQQE3JDfOcJ02WGh+2J05GBqn+pFsJLAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-31T20:21:47.049326Z","bundle_sha256":"6e75b56ff9cc31b1e45c5163740c0c0bad64e125aafb4c0709a148082c0e3689"}}