{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:BLBJZZ3XYGSKIL3TPMWSJKBAYO","short_pith_number":"pith:BLBJZZ3X","canonical_record":{"source":{"id":"1906.01140","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-04T00:33:56Z","cross_cats_sorted":["cs.AI","cs.LG","cs.RO"],"title_canon_sha256":"8999d6407489e09e705532f6efcc62b8efc9a9bcef1481f9563f7d00de94fc6b","abstract_canon_sha256":"dfb2ece6ed520515ec2cdf21ec3f95c0b09f07483dfa0e411c19238c95f332ca"},"schema_version":"1.0"},"canonical_sha256":"0ac29ce777c1a4a42f737b2d24a820c3a0e8ecb6dc14b2f165c9531c7c8ed23d","source":{"kind":"arxiv","id":"1906.01140","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.01140","created_at":"2026-07-05T00:02:27Z"},{"alias_kind":"arxiv_version","alias_value":"1906.01140v2","created_at":"2026-07-05T00:02:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.01140","created_at":"2026-07-05T00:02:27Z"},{"alias_kind":"pith_short_12","alias_value":"BLBJZZ3XYGSK","created_at":"2026-07-05T00:02:27Z"},{"alias_kind":"pith_short_16","alias_value":"BLBJZZ3XYGSKIL3T","created_at":"2026-07-05T00:02:27Z"},{"alias_kind":"pith_short_8","alias_value":"BLBJZZ3X","created_at":"2026-07-05T00:02:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:BLBJZZ3XYGSKIL3TPMWSJKBAYO","target":"record","payload":{"canonical_record":{"source":{"id":"1906.01140","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-04T00:33:56Z","cross_cats_sorted":["cs.AI","cs.LG","cs.RO"],"title_canon_sha256":"8999d6407489e09e705532f6efcc62b8efc9a9bcef1481f9563f7d00de94fc6b","abstract_canon_sha256":"dfb2ece6ed520515ec2cdf21ec3f95c0b09f07483dfa0e411c19238c95f332ca"},"schema_version":"1.0"},"canonical_sha256":"0ac29ce777c1a4a42f737b2d24a820c3a0e8ecb6dc14b2f165c9531c7c8ed23d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:02:27.277511Z","signature_b64":"ErlnFbamPT5FrRDuJb8sZqteqOgCFGfcegKw+9G1EUY6hZEAiSHlkkAXo4t19mx6Lp/kHa0JdAPdHw8oVYs+CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0ac29ce777c1a4a42f737b2d24a820c3a0e8ecb6dc14b2f165c9531c7c8ed23d","last_reissued_at":"2026-07-05T00:02:27.276947Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:02:27.276947Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1906.01140","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":"2026-07-05T00:02:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"McXlDi1ZP0uZi/VvZ+yUR8G+LtzpNitlMl8rQwlGuqJVM9E1y2JPsXEkXSt9qr3VbwHEqJFhJcmteky5dT9aDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T07:28:58.494604Z"},"content_sha256":"6dbff38c0bf2351e70c9d097af96daa6ec8ae9c91ca61facad589e3d21d6c92f","schema_version":"1.0","event_id":"sha256:6dbff38c0bf2351e70c9d097af96daa6ec8ae9c91ca61facad589e3d21d6c92f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:BLBJZZ3XYGSKIL3TPMWSJKBAYO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.RO"],"primary_cat":"cs.CV","authors_text":"Andrew Markham, Bo Yang, Jianan Wang, Niki Trigoni, Qingyong Hu, Ronald Clark, Sen Wang","submitted_at":"2019-06-04T00:33:56Z","abstract_excerpt":"We propose a novel, conceptually simple and general framework for instance segmentation on 3D point clouds. Our method, called 3D-BoNet, follows the simple design philosophy of per-point multilayer perceptrons (MLPs). The framework directly regresses 3D bounding boxes for all instances in a point cloud, while simultaneously predicting a point-level mask for each instance. It consists of a backbone network followed by two parallel network branches for 1) bounding box regression and 2) point mask prediction. 3D-BoNet is single-stage, anchor-free and end-to-end trainable. Moreover, it is remarkab"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.01140","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/1906.01140/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":"2026-07-05T00:02:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bKw/2WbSuxtrBT4yTFDqpXQMhCh4/bIESz21zMtLHWy3fx7qy1KT88lkWJj0lZyp5hqSPn3ALEkZe6yq8ut/Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T07:28:58.495228Z"},"content_sha256":"0838237cbdd7ab997f20e420f8ba43573a4cf01e211f939c396e4b13f220a47c","schema_version":"1.0","event_id":"sha256:0838237cbdd7ab997f20e420f8ba43573a4cf01e211f939c396e4b13f220a47c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BLBJZZ3XYGSKIL3TPMWSJKBAYO/bundle.json","state_url":"https://pith.science/pith/BLBJZZ3XYGSKIL3TPMWSJKBAYO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BLBJZZ3XYGSKIL3TPMWSJKBAYO/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":"2026-08-15T07:28:58Z","links":{"resolver":"https://pith.science/pith/BLBJZZ3XYGSKIL3TPMWSJKBAYO","bundle":"https://pith.science/pith/BLBJZZ3XYGSKIL3TPMWSJKBAYO/bundle.json","state":"https://pith.science/pith/BLBJZZ3XYGSKIL3TPMWSJKBAYO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BLBJZZ3XYGSKIL3TPMWSJKBAYO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:BLBJZZ3XYGSKIL3TPMWSJKBAYO","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":"dfb2ece6ed520515ec2cdf21ec3f95c0b09f07483dfa0e411c19238c95f332ca","cross_cats_sorted":["cs.AI","cs.LG","cs.RO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-04T00:33:56Z","title_canon_sha256":"8999d6407489e09e705532f6efcc62b8efc9a9bcef1481f9563f7d00de94fc6b"},"schema_version":"1.0","source":{"id":"1906.01140","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.01140","created_at":"2026-07-05T00:02:27Z"},{"alias_kind":"arxiv_version","alias_value":"1906.01140v2","created_at":"2026-07-05T00:02:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.01140","created_at":"2026-07-05T00:02:27Z"},{"alias_kind":"pith_short_12","alias_value":"BLBJZZ3XYGSK","created_at":"2026-07-05T00:02:27Z"},{"alias_kind":"pith_short_16","alias_value":"BLBJZZ3XYGSKIL3T","created_at":"2026-07-05T00:02:27Z"},{"alias_kind":"pith_short_8","alias_value":"BLBJZZ3X","created_at":"2026-07-05T00:02:27Z"}],"graph_snapshots":[{"event_id":"sha256:0838237cbdd7ab997f20e420f8ba43573a4cf01e211f939c396e4b13f220a47c","target":"graph","created_at":"2026-07-05T00:02:27Z","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/1906.01140/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We propose a novel, conceptually simple and general framework for instance segmentation on 3D point clouds. Our method, called 3D-BoNet, follows the simple design philosophy of per-point multilayer perceptrons (MLPs). The framework directly regresses 3D bounding boxes for all instances in a point cloud, while simultaneously predicting a point-level mask for each instance. It consists of a backbone network followed by two parallel network branches for 1) bounding box regression and 2) point mask prediction. 3D-BoNet is single-stage, anchor-free and end-to-end trainable. Moreover, it is remarkab","authors_text":"Andrew Markham, Bo Yang, Jianan Wang, Niki Trigoni, Qingyong Hu, Ronald Clark, Sen Wang","cross_cats":["cs.AI","cs.LG","cs.RO"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-04T00:33:56Z","title":"Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.01140","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":"sha256:6dbff38c0bf2351e70c9d097af96daa6ec8ae9c91ca61facad589e3d21d6c92f","target":"record","created_at":"2026-07-05T00:02:27Z","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":"dfb2ece6ed520515ec2cdf21ec3f95c0b09f07483dfa0e411c19238c95f332ca","cross_cats_sorted":["cs.AI","cs.LG","cs.RO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-04T00:33:56Z","title_canon_sha256":"8999d6407489e09e705532f6efcc62b8efc9a9bcef1481f9563f7d00de94fc6b"},"schema_version":"1.0","source":{"id":"1906.01140","kind":"arxiv","version":2}},"canonical_sha256":"0ac29ce777c1a4a42f737b2d24a820c3a0e8ecb6dc14b2f165c9531c7c8ed23d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0ac29ce777c1a4a42f737b2d24a820c3a0e8ecb6dc14b2f165c9531c7c8ed23d","first_computed_at":"2026-07-05T00:02:27.276947Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:02:27.276947Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ErlnFbamPT5FrRDuJb8sZqteqOgCFGfcegKw+9G1EUY6hZEAiSHlkkAXo4t19mx6Lp/kHa0JdAPdHw8oVYs+CA==","signature_status":"signed_v1","signed_at":"2026-07-05T00:02:27.277511Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.01140","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6dbff38c0bf2351e70c9d097af96daa6ec8ae9c91ca61facad589e3d21d6c92f","sha256:0838237cbdd7ab997f20e420f8ba43573a4cf01e211f939c396e4b13f220a47c"],"state_sha256":"a94b35c3e838f6849ca102a8dac6633b6eaf142d19fc8a561a1420c93ce391fc"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zXbrne+ao1Z67VmCTfm6rRTQICyPR78qKv57nygDWrYlseSuxx7oiZlgWy+b0ZPF3lhiPEPkb3FUw/UEgWI4Dg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T07:28:58.500635Z","bundle_sha256":"ed6a4456d57e565cda789368f5ce45b5a7c86ca5068e289f5256b8581f840161"}}