{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:ODSF6OCVJYUXHLQFFCAP6GJJI7","short_pith_number":"pith:ODSF6OCV","canonical_record":{"source":{"id":"2103.03764","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-03-05T15:46:47Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"19d05131e8e59315a18ecb2eba2725d9e09b9313434eed29a0850977e207706a","abstract_canon_sha256":"b406f93ee85933d981cd19afdb19c5f13ff666cf3b2f57c828d86778b21dd8c7"},"schema_version":"1.0"},"canonical_sha256":"70e45f38554e2973ae052880ff192947edc3fcba2da1f8c984b3c4a7699d4b1f","source":{"kind":"arxiv","id":"2103.03764","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2103.03764","created_at":"2026-07-05T02:20:39Z"},{"alias_kind":"arxiv_version","alias_value":"2103.03764v1","created_at":"2026-07-05T02:20:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.03764","created_at":"2026-07-05T02:20:39Z"},{"alias_kind":"pith_short_12","alias_value":"ODSF6OCVJYUX","created_at":"2026-07-05T02:20:39Z"},{"alias_kind":"pith_short_16","alias_value":"ODSF6OCVJYUXHLQF","created_at":"2026-07-05T02:20:39Z"},{"alias_kind":"pith_short_8","alias_value":"ODSF6OCV","created_at":"2026-07-05T02:20:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:ODSF6OCVJYUXHLQFFCAP6GJJI7","target":"record","payload":{"canonical_record":{"source":{"id":"2103.03764","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-03-05T15:46:47Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"19d05131e8e59315a18ecb2eba2725d9e09b9313434eed29a0850977e207706a","abstract_canon_sha256":"b406f93ee85933d981cd19afdb19c5f13ff666cf3b2f57c828d86778b21dd8c7"},"schema_version":"1.0"},"canonical_sha256":"70e45f38554e2973ae052880ff192947edc3fcba2da1f8c984b3c4a7699d4b1f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:20:39.607233Z","signature_b64":"7sguySILbAzWDuCVnS5TlsSoo8KFwwOhFB0F8uWtSkA++xipQ6AFfW1nBF/cxLlHK3mz+dQGgt20KzmaV5vqDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"70e45f38554e2973ae052880ff192947edc3fcba2da1f8c984b3c4a7699d4b1f","last_reissued_at":"2026-07-05T02:20:39.606823Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:20:39.606823Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2103.03764","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":"2026-07-05T02:20:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IAplgPXSTZD/uxoRGczGVY3lra1I27DdbQ1LLWI+BsYRWFmq2Yp5Co7lDC8krmcsEXvd5sbPkCCP8axUUT3XDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T12:17:25.657720Z"},"content_sha256":"78dbdd3621975ed67cb08cb2417aabafce9694eeec2d5c76bde2bc09b274bf1b","schema_version":"1.0","event_id":"sha256:78dbdd3621975ed67cb08cb2417aabafce9694eeec2d5c76bde2bc09b274bf1b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:ODSF6OCVJYUXHLQFFCAP6GJJI7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Convolutional Architecture for 3D Model Embedding","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Arniel Labrada, Benjamin Bustos, Ivan Sipiran","submitted_at":"2021-03-05T15:46:47Z","abstract_excerpt":"During the last years, many advances have been made in tasks like3D model retrieval, 3D model classification, and 3D model segmentation.The typical 3D representations such as point clouds, voxels, and poly-gon meshes are mostly suitable for rendering purposes, while their use forcognitive processes (retrieval, classification, segmentation) is limited dueto their high redundancy and complexity. We propose a deep learningarchitecture to handle 3D models as an input. We combine this architec-ture with other standard architectures like Convolutional Neural Networksand autoencoders for computing 3D"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.03764","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/2103.03764/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":"2026-07-05T02:20:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WRsmapITBTtpp2L/VrhcO89QXQgmVBL3SvO80qIC9r1g1mIfpck/Ip78kZzCeIiQvji09nbIFKIUaqejvQ3YDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T12:17:25.658852Z"},"content_sha256":"914a86032d8c08cc47052d20c38c603b9e19c12aea296895e9a66fd76b2035ae","schema_version":"1.0","event_id":"sha256:914a86032d8c08cc47052d20c38c603b9e19c12aea296895e9a66fd76b2035ae"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ODSF6OCVJYUXHLQFFCAP6GJJI7/bundle.json","state_url":"https://pith.science/pith/ODSF6OCVJYUXHLQFFCAP6GJJI7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ODSF6OCVJYUXHLQFFCAP6GJJI7/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":"2026-08-07T12:17:25Z","links":{"resolver":"https://pith.science/pith/ODSF6OCVJYUXHLQFFCAP6GJJI7","bundle":"https://pith.science/pith/ODSF6OCVJYUXHLQFFCAP6GJJI7/bundle.json","state":"https://pith.science/pith/ODSF6OCVJYUXHLQFFCAP6GJJI7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ODSF6OCVJYUXHLQFFCAP6GJJI7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:ODSF6OCVJYUXHLQFFCAP6GJJI7","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":"b406f93ee85933d981cd19afdb19c5f13ff666cf3b2f57c828d86778b21dd8c7","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-03-05T15:46:47Z","title_canon_sha256":"19d05131e8e59315a18ecb2eba2725d9e09b9313434eed29a0850977e207706a"},"schema_version":"1.0","source":{"id":"2103.03764","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2103.03764","created_at":"2026-07-05T02:20:39Z"},{"alias_kind":"arxiv_version","alias_value":"2103.03764v1","created_at":"2026-07-05T02:20:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.03764","created_at":"2026-07-05T02:20:39Z"},{"alias_kind":"pith_short_12","alias_value":"ODSF6OCVJYUX","created_at":"2026-07-05T02:20:39Z"},{"alias_kind":"pith_short_16","alias_value":"ODSF6OCVJYUXHLQF","created_at":"2026-07-05T02:20:39Z"},{"alias_kind":"pith_short_8","alias_value":"ODSF6OCV","created_at":"2026-07-05T02:20:39Z"}],"graph_snapshots":[{"event_id":"sha256:914a86032d8c08cc47052d20c38c603b9e19c12aea296895e9a66fd76b2035ae","target":"graph","created_at":"2026-07-05T02:20:39Z","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/2103.03764/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"During the last years, many advances have been made in tasks like3D model retrieval, 3D model classification, and 3D model segmentation.The typical 3D representations such as point clouds, voxels, and poly-gon meshes are mostly suitable for rendering purposes, while their use forcognitive processes (retrieval, classification, segmentation) is limited dueto their high redundancy and complexity. We propose a deep learningarchitecture to handle 3D models as an input. We combine this architec-ture with other standard architectures like Convolutional Neural Networksand autoencoders for computing 3D","authors_text":"Arniel Labrada, Benjamin Bustos, Ivan Sipiran","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-03-05T15:46:47Z","title":"A Convolutional Architecture for 3D Model Embedding"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.03764","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":"sha256:78dbdd3621975ed67cb08cb2417aabafce9694eeec2d5c76bde2bc09b274bf1b","target":"record","created_at":"2026-07-05T02:20:39Z","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":"b406f93ee85933d981cd19afdb19c5f13ff666cf3b2f57c828d86778b21dd8c7","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-03-05T15:46:47Z","title_canon_sha256":"19d05131e8e59315a18ecb2eba2725d9e09b9313434eed29a0850977e207706a"},"schema_version":"1.0","source":{"id":"2103.03764","kind":"arxiv","version":1}},"canonical_sha256":"70e45f38554e2973ae052880ff192947edc3fcba2da1f8c984b3c4a7699d4b1f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"70e45f38554e2973ae052880ff192947edc3fcba2da1f8c984b3c4a7699d4b1f","first_computed_at":"2026-07-05T02:20:39.606823Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:20:39.606823Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7sguySILbAzWDuCVnS5TlsSoo8KFwwOhFB0F8uWtSkA++xipQ6AFfW1nBF/cxLlHK3mz+dQGgt20KzmaV5vqDg==","signature_status":"signed_v1","signed_at":"2026-07-05T02:20:39.607233Z","signed_message":"canonical_sha256_bytes"},"source_id":"2103.03764","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:78dbdd3621975ed67cb08cb2417aabafce9694eeec2d5c76bde2bc09b274bf1b","sha256:914a86032d8c08cc47052d20c38c603b9e19c12aea296895e9a66fd76b2035ae"],"state_sha256":"d78c11033e774a42d382ba130bdc00d26d3d8c196e8945ff20eca8110dbf4732"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TnSR8YkuMjYE5gmv/wMhuEXIlFlAIB14GtuVR2qxldVg/MX6D9uD/sZDzYhrAi+EfKmkN+Uwi/hP7Q59mdMxBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T12:17:25.666507Z","bundle_sha256":"520cbb24ea5ea61184cfad5e1aabd676efd40feaaa8cc563c2093f6fd547cb9e"}}