{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:ITG4H5RHTLDYFLV5E4DPUF2SJK","short_pith_number":"pith:ITG4H5RH","canonical_record":{"source":{"id":"2306.15919","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-06-28T04:48:21Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"a865c65f5df266c95868f5b1a896e11f23bc102062d07e06a727cc4c4b41a601","abstract_canon_sha256":"463c41e3ffe1e00fc9e85a70a23f8408d798ffa05b8b0ff0e6ae71193ecf20ce"},"schema_version":"1.0"},"canonical_sha256":"44cdc3f6279ac782aebd2706fa17524a8fcce5f638e3688c93ed93a63a5cc5ac","source":{"kind":"arxiv","id":"2306.15919","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.15919","created_at":"2026-07-05T06:25:46Z"},{"alias_kind":"arxiv_version","alias_value":"2306.15919v1","created_at":"2026-07-05T06:25:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.15919","created_at":"2026-07-05T06:25:46Z"},{"alias_kind":"pith_short_12","alias_value":"ITG4H5RHTLDY","created_at":"2026-07-05T06:25:46Z"},{"alias_kind":"pith_short_16","alias_value":"ITG4H5RHTLDYFLV5","created_at":"2026-07-05T06:25:46Z"},{"alias_kind":"pith_short_8","alias_value":"ITG4H5RH","created_at":"2026-07-05T06:25:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:ITG4H5RHTLDYFLV5E4DPUF2SJK","target":"record","payload":{"canonical_record":{"source":{"id":"2306.15919","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-06-28T04:48:21Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"a865c65f5df266c95868f5b1a896e11f23bc102062d07e06a727cc4c4b41a601","abstract_canon_sha256":"463c41e3ffe1e00fc9e85a70a23f8408d798ffa05b8b0ff0e6ae71193ecf20ce"},"schema_version":"1.0"},"canonical_sha256":"44cdc3f6279ac782aebd2706fa17524a8fcce5f638e3688c93ed93a63a5cc5ac","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:25:46.423180Z","signature_b64":"FmcHoEwImmnSZ3jtxqudlTAyOO3N5qfHCy+8ewWQTWC16XviNnYaoPY9xhvCbnqqf/0WixFcn2jV76/iNWrxCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"44cdc3f6279ac782aebd2706fa17524a8fcce5f638e3688c93ed93a63a5cc5ac","last_reissued_at":"2026-07-05T06:25:46.422629Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:25:46.422629Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2306.15919","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":"2026-07-05T06:25:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7yPjcwocpJE7FBoA5lAXQ/OK5kXbrdooLFbUN5QV02MSl4SyMZfx+Kwb2empa8qSRDtJ4kUmIbfN64EtVpRmAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T16:19:06.123550Z"},"content_sha256":"f86d764966e1971c60db82dd46f43e8fd8786443a9cd335195cbcc1982bd3b4e","schema_version":"1.0","event_id":"sha256:f86d764966e1971c60db82dd46f43e8fd8786443a9cd335195cbcc1982bd3b4e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:ITG4H5RHTLDYFLV5E4DPUF2SJK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Fine-grained 3D object recognition: an approach and experiments","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Hamidreza Kasaei, Junhyung Jo","submitted_at":"2023-06-28T04:48:21Z","abstract_excerpt":"Three-dimensional (3D) object recognition technology is being used as a core technology in advanced technologies such as autonomous driving of automobiles. There are two sets of approaches for 3D object recognition: (i) hand-crafted approaches like Global Orthographic Object Descriptor (GOOD), and (ii) deep learning-based approaches such as MobileNet and VGG. However, it is needed to know which of these approaches works better in an open-ended domain where the number of known categories increases over time, and the system should learn about new object categories using few training examples. In"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.15919","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/2306.15919/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":"2026-07-05T06:25:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Os2R+ooA/aC+r2IG9xOoT4hZ0KOuBdrY1tOv1+cwXxUuBsYMcBB3ypY86hCVSpthg0B+Mt/1zeHO2lb3MjsVAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T16:19:06.123920Z"},"content_sha256":"58d69913693f65532f493a19e49ff8aabfcdac619803661115ce604527c57bf5","schema_version":"1.0","event_id":"sha256:58d69913693f65532f493a19e49ff8aabfcdac619803661115ce604527c57bf5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ITG4H5RHTLDYFLV5E4DPUF2SJK/bundle.json","state_url":"https://pith.science/pith/ITG4H5RHTLDYFLV5E4DPUF2SJK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ITG4H5RHTLDYFLV5E4DPUF2SJK/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":"2026-07-06T16:19:06Z","links":{"resolver":"https://pith.science/pith/ITG4H5RHTLDYFLV5E4DPUF2SJK","bundle":"https://pith.science/pith/ITG4H5RHTLDYFLV5E4DPUF2SJK/bundle.json","state":"https://pith.science/pith/ITG4H5RHTLDYFLV5E4DPUF2SJK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ITG4H5RHTLDYFLV5E4DPUF2SJK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:ITG4H5RHTLDYFLV5E4DPUF2SJK","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":"463c41e3ffe1e00fc9e85a70a23f8408d798ffa05b8b0ff0e6ae71193ecf20ce","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-06-28T04:48:21Z","title_canon_sha256":"a865c65f5df266c95868f5b1a896e11f23bc102062d07e06a727cc4c4b41a601"},"schema_version":"1.0","source":{"id":"2306.15919","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.15919","created_at":"2026-07-05T06:25:46Z"},{"alias_kind":"arxiv_version","alias_value":"2306.15919v1","created_at":"2026-07-05T06:25:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.15919","created_at":"2026-07-05T06:25:46Z"},{"alias_kind":"pith_short_12","alias_value":"ITG4H5RHTLDY","created_at":"2026-07-05T06:25:46Z"},{"alias_kind":"pith_short_16","alias_value":"ITG4H5RHTLDYFLV5","created_at":"2026-07-05T06:25:46Z"},{"alias_kind":"pith_short_8","alias_value":"ITG4H5RH","created_at":"2026-07-05T06:25:46Z"}],"graph_snapshots":[{"event_id":"sha256:58d69913693f65532f493a19e49ff8aabfcdac619803661115ce604527c57bf5","target":"graph","created_at":"2026-07-05T06:25:46Z","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/2306.15919/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Three-dimensional (3D) object recognition technology is being used as a core technology in advanced technologies such as autonomous driving of automobiles. There are two sets of approaches for 3D object recognition: (i) hand-crafted approaches like Global Orthographic Object Descriptor (GOOD), and (ii) deep learning-based approaches such as MobileNet and VGG. However, it is needed to know which of these approaches works better in an open-ended domain where the number of known categories increases over time, and the system should learn about new object categories using few training examples. In","authors_text":"Hamidreza Kasaei, Junhyung Jo","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-06-28T04:48:21Z","title":"Fine-grained 3D object recognition: an approach and experiments"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.15919","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:f86d764966e1971c60db82dd46f43e8fd8786443a9cd335195cbcc1982bd3b4e","target":"record","created_at":"2026-07-05T06:25:46Z","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":"463c41e3ffe1e00fc9e85a70a23f8408d798ffa05b8b0ff0e6ae71193ecf20ce","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-06-28T04:48:21Z","title_canon_sha256":"a865c65f5df266c95868f5b1a896e11f23bc102062d07e06a727cc4c4b41a601"},"schema_version":"1.0","source":{"id":"2306.15919","kind":"arxiv","version":1}},"canonical_sha256":"44cdc3f6279ac782aebd2706fa17524a8fcce5f638e3688c93ed93a63a5cc5ac","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"44cdc3f6279ac782aebd2706fa17524a8fcce5f638e3688c93ed93a63a5cc5ac","first_computed_at":"2026-07-05T06:25:46.422629Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:25:46.422629Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FmcHoEwImmnSZ3jtxqudlTAyOO3N5qfHCy+8ewWQTWC16XviNnYaoPY9xhvCbnqqf/0WixFcn2jV76/iNWrxCg==","signature_status":"signed_v1","signed_at":"2026-07-05T06:25:46.423180Z","signed_message":"canonical_sha256_bytes"},"source_id":"2306.15919","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f86d764966e1971c60db82dd46f43e8fd8786443a9cd335195cbcc1982bd3b4e","sha256:58d69913693f65532f493a19e49ff8aabfcdac619803661115ce604527c57bf5"],"state_sha256":"dab70b05f9211e5906aa04eab58773de0661692c558fe07a5b690df4ed1946d8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MMjLe3Wm3ryzsA4wJqEdTb7ftuDOmCiVEWwmb0VvbrxxADw9ZqKGNmX7bJ6efYiLfPyvRTusdsJTspjE6SqEAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T16:19:06.125920Z","bundle_sha256":"951ef1200ef501ab8d3462b0a79543cb3cddfb443756f1c421e9ee17730fc356"}}