{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:JPQIQPEYXNTYT6GHJC6DOATBWA","short_pith_number":"pith:JPQIQPEY","canonical_record":{"source":{"id":"1811.11209","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-27T19:22:24Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"12fec16b5e3627b49e78ff47e2dff697f7513be9e982e296c16c9446d3649973","abstract_canon_sha256":"f0d36b64bddcd739265f885aa104a98497a6ec6e614ec24e532b30fdeb8386f4"},"schema_version":"1.0"},"canonical_sha256":"4be0883c98bb6789f8c748bc370261b023c4c7266bd2259dc5c9128785540a77","source":{"kind":"arxiv","id":"1811.11209","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.11209","created_at":"2026-07-05T00:13:00Z"},{"alias_kind":"arxiv_version","alias_value":"1811.11209v2","created_at":"2026-07-05T00:13:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.11209","created_at":"2026-07-05T00:13:00Z"},{"alias_kind":"pith_short_12","alias_value":"JPQIQPEYXNTY","created_at":"2026-07-05T00:13:00Z"},{"alias_kind":"pith_short_16","alias_value":"JPQIQPEYXNTYT6GH","created_at":"2026-07-05T00:13:00Z"},{"alias_kind":"pith_short_8","alias_value":"JPQIQPEY","created_at":"2026-07-05T00:13:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:JPQIQPEYXNTYT6GHJC6DOATBWA","target":"record","payload":{"canonical_record":{"source":{"id":"1811.11209","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-27T19:22:24Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"12fec16b5e3627b49e78ff47e2dff697f7513be9e982e296c16c9446d3649973","abstract_canon_sha256":"f0d36b64bddcd739265f885aa104a98497a6ec6e614ec24e532b30fdeb8386f4"},"schema_version":"1.0"},"canonical_sha256":"4be0883c98bb6789f8c748bc370261b023c4c7266bd2259dc5c9128785540a77","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:13:00.772863Z","signature_b64":"/G5NYeHqbf6hx9PKwWeF2yZYE56bEG/sA2liPi6JbzbvLYAbbwjkr2BsRPuk6ACHovJk0ISMzNwCx18O8scyDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4be0883c98bb6789f8c748bc370261b023c4c7266bd2259dc5c9128785540a77","last_reissued_at":"2026-07-05T00:13:00.772451Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:13:00.772451Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1811.11209","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:13:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SgUpK5ZURe24amU0uG2nxw7ktacquEDbSvIWKqX/Xi2YGI62WKLJ9RMW3mEv2f8OLNAarZZFc1kqoUxlLJc3Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T23:09:34.065559Z"},"content_sha256":"24c0f48147a2be68bfafc925c4d237c80895c3dcd54143aa31641c01ef0f425d","schema_version":"1.0","event_id":"sha256:24c0f48147a2be68bfafc925c4d237c80895c3dcd54143aa31641c01ef0f425d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:JPQIQPEYXNTYT6GHJC6DOATBWA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Iterative Transformer Network for 3D Point Cloud","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Christoph Mertz, David Held, Martial Hebert, Wentao Yuan","submitted_at":"2018-11-27T19:22:24Z","abstract_excerpt":"3D point cloud is an efficient and flexible representation of 3D structures. Recently, neural networks operating on point clouds have shown superior performance on 3D understanding tasks such as shape classification and part segmentation. However, performance on such tasks is evaluated on complete shapes aligned in a canonical frame, while real world 3D data are partial and unaligned. A key challenge in learning from partial, unaligned point cloud data is to learn features that are invariant or equivariant with respect to geometric transformations. To address this challenge, we propose the Ite"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.11209","kind":"arxiv","version":2},"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/1811.11209/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":"2026-07-05T00:13:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZA8sDhxfqUWD9Fq/YDysUe2KvZdAHPMuJjLg+6Rz+Sdg33cp4gbyelxCUANIDY8Oh9kpvgNA0MlikD0kerBJDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T23:09:34.066105Z"},"content_sha256":"4f829a558851c962f772efb72dfc5c518202867ac154673fec85026dc932af03","schema_version":"1.0","event_id":"sha256:4f829a558851c962f772efb72dfc5c518202867ac154673fec85026dc932af03"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JPQIQPEYXNTYT6GHJC6DOATBWA/bundle.json","state_url":"https://pith.science/pith/JPQIQPEYXNTYT6GHJC6DOATBWA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JPQIQPEYXNTYT6GHJC6DOATBWA/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":"2026-08-08T23:09:34Z","links":{"resolver":"https://pith.science/pith/JPQIQPEYXNTYT6GHJC6DOATBWA","bundle":"https://pith.science/pith/JPQIQPEYXNTYT6GHJC6DOATBWA/bundle.json","state":"https://pith.science/pith/JPQIQPEYXNTYT6GHJC6DOATBWA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JPQIQPEYXNTYT6GHJC6DOATBWA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:JPQIQPEYXNTYT6GHJC6DOATBWA","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":"f0d36b64bddcd739265f885aa104a98497a6ec6e614ec24e532b30fdeb8386f4","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-27T19:22:24Z","title_canon_sha256":"12fec16b5e3627b49e78ff47e2dff697f7513be9e982e296c16c9446d3649973"},"schema_version":"1.0","source":{"id":"1811.11209","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.11209","created_at":"2026-07-05T00:13:00Z"},{"alias_kind":"arxiv_version","alias_value":"1811.11209v2","created_at":"2026-07-05T00:13:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.11209","created_at":"2026-07-05T00:13:00Z"},{"alias_kind":"pith_short_12","alias_value":"JPQIQPEYXNTY","created_at":"2026-07-05T00:13:00Z"},{"alias_kind":"pith_short_16","alias_value":"JPQIQPEYXNTYT6GH","created_at":"2026-07-05T00:13:00Z"},{"alias_kind":"pith_short_8","alias_value":"JPQIQPEY","created_at":"2026-07-05T00:13:00Z"}],"graph_snapshots":[{"event_id":"sha256:4f829a558851c962f772efb72dfc5c518202867ac154673fec85026dc932af03","target":"graph","created_at":"2026-07-05T00:13:00Z","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/1811.11209/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"3D point cloud is an efficient and flexible representation of 3D structures. Recently, neural networks operating on point clouds have shown superior performance on 3D understanding tasks such as shape classification and part segmentation. However, performance on such tasks is evaluated on complete shapes aligned in a canonical frame, while real world 3D data are partial and unaligned. A key challenge in learning from partial, unaligned point cloud data is to learn features that are invariant or equivariant with respect to geometric transformations. To address this challenge, we propose the Ite","authors_text":"Christoph Mertz, David Held, Martial Hebert, Wentao Yuan","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-27T19:22:24Z","title":"Iterative Transformer Network for 3D Point Cloud"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.11209","kind":"arxiv","version":2},"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":"sha256:24c0f48147a2be68bfafc925c4d237c80895c3dcd54143aa31641c01ef0f425d","target":"record","created_at":"2026-07-05T00:13:00Z","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":"f0d36b64bddcd739265f885aa104a98497a6ec6e614ec24e532b30fdeb8386f4","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-27T19:22:24Z","title_canon_sha256":"12fec16b5e3627b49e78ff47e2dff697f7513be9e982e296c16c9446d3649973"},"schema_version":"1.0","source":{"id":"1811.11209","kind":"arxiv","version":2}},"canonical_sha256":"4be0883c98bb6789f8c748bc370261b023c4c7266bd2259dc5c9128785540a77","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4be0883c98bb6789f8c748bc370261b023c4c7266bd2259dc5c9128785540a77","first_computed_at":"2026-07-05T00:13:00.772451Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:13:00.772451Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/G5NYeHqbf6hx9PKwWeF2yZYE56bEG/sA2liPi6JbzbvLYAbbwjkr2BsRPuk6ACHovJk0ISMzNwCx18O8scyDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T00:13:00.772863Z","signed_message":"canonical_sha256_bytes"},"source_id":"1811.11209","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:24c0f48147a2be68bfafc925c4d237c80895c3dcd54143aa31641c01ef0f425d","sha256:4f829a558851c962f772efb72dfc5c518202867ac154673fec85026dc932af03"],"state_sha256":"70f23516aa880cb5ddb4a3016d472be2701a25911875abccfd5da8ec70b6397e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wf3eiT8vYIG3OudEMnlw+3A+qS1f2I5BIaWaKIWTU0U1u7jQG8/n5r9zxpQrOhhcbAqFJOLraMMbJPbl2irvCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T23:09:34.071524Z","bundle_sha256":"25874bf5d558a37bb585b864f6f4dcf9cd33bd56371ee2e130f716f718b2eb9a"}}