{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:57NMV5ULYSENMA556BXFHT6RYM","short_pith_number":"pith:57NMV5UL","canonical_record":{"source":{"id":"1807.08894","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-07-24T03:42:53Z","cross_cats_sorted":["cs.AI","cs.CV","cs.LG"],"title_canon_sha256":"50539cb569078f22ef2e604a14fbc7c6d852c0279e64e257f9c832af10014e99","abstract_canon_sha256":"40c1d06e99d9fce73d2af9e3ccd25635f4f948845066275eeff796752de078af"},"schema_version":"1.0"},"canonical_sha256":"efdacaf68bc488d603bdf06e53cfd1c30f9f0267b7759cdcd39e8f6aa682c6c6","source":{"kind":"arxiv","id":"1807.08894","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.08894","created_at":"2026-05-18T00:05:21Z"},{"alias_kind":"arxiv_version","alias_value":"1807.08894v2","created_at":"2026-05-18T00:05:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.08894","created_at":"2026-05-18T00:05:21Z"},{"alias_kind":"pith_short_12","alias_value":"57NMV5ULYSEN","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_16","alias_value":"57NMV5ULYSENMA55","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_8","alias_value":"57NMV5UL","created_at":"2026-05-18T12:32:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:57NMV5ULYSENMA556BXFHT6RYM","target":"record","payload":{"canonical_record":{"source":{"id":"1807.08894","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-07-24T03:42:53Z","cross_cats_sorted":["cs.AI","cs.CV","cs.LG"],"title_canon_sha256":"50539cb569078f22ef2e604a14fbc7c6d852c0279e64e257f9c832af10014e99","abstract_canon_sha256":"40c1d06e99d9fce73d2af9e3ccd25635f4f948845066275eeff796752de078af"},"schema_version":"1.0"},"canonical_sha256":"efdacaf68bc488d603bdf06e53cfd1c30f9f0267b7759cdcd39e8f6aa682c6c6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:05:21.415099Z","signature_b64":"sm0066N3cgxSNlz6uSHQRlUeRtC2RVFaS7JLTTROghoU7MIuU3jpeCpKD3I1m+I6a/M0XVja/anlXIMnhTLBBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"efdacaf68bc488d603bdf06e53cfd1c30f9f0267b7759cdcd39e8f6aa682c6c6","last_reissued_at":"2026-05-18T00:05:21.414666Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:05:21.414666Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1807.08894","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":"2026-05-18T00:05:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8lls1COSXydtIOa4+FaiXtjZS7GcQ1O6Tgs/rhF7LDvDCrOLD2XkJNhsBeKzsVhRXxNnHZnQpUJvxGhs4eEkCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T20:59:47.407202Z"},"content_sha256":"4d28c4e698172b60d611e9f79d5fda73109fd9e842ee7cb69b79b98a21fd4257","schema_version":"1.0","event_id":"sha256:4d28c4e698172b60d611e9f79d5fda73109fd9e842ee7cb69b79b98a21fd4257"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:57NMV5ULYSENMA556BXFHT6RYM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ClusterNet: 3D Instance Segmentation in RGB-D Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.LG"],"primary_cat":"cs.RO","authors_text":"Jeannette Bohg, Lin Shao, Ye Tian","submitted_at":"2018-07-24T03:42:53Z","abstract_excerpt":"We propose a method for instance-level segmentation that uses RGB-D data as input and provides detailed information about the location, geometry and number of individual objects in the scene. This level of understanding is fundamental for autonomous robots. It enables safe and robust decision-making under the large uncertainty of the real-world. In our model, we propose to use the first and second order moments of the object occupancy function to represent an object instance. We train an hourglass Deep Neural Network (DNN) where each pixel in the output votes for the 3D position of the corresp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.08894","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":""},"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":"2026-05-18T00:05:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9CH26QZRKLpo8fn+aiPA2Lqp0Avdu71qHq/7mfqT2W/G18Z7y8Jr2HOKixYjblT1OjiiCzTBw8c1XASvUVvbBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T20:59:47.407845Z"},"content_sha256":"0c4b54a76b46a06b6b359f058c886dcd602186d934f45bf7f625e842e36f9397","schema_version":"1.0","event_id":"sha256:0c4b54a76b46a06b6b359f058c886dcd602186d934f45bf7f625e842e36f9397"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/57NMV5ULYSENMA556BXFHT6RYM/bundle.json","state_url":"https://pith.science/pith/57NMV5ULYSENMA556BXFHT6RYM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/57NMV5ULYSENMA556BXFHT6RYM/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":"2026-05-25T20:59:47Z","links":{"resolver":"https://pith.science/pith/57NMV5ULYSENMA556BXFHT6RYM","bundle":"https://pith.science/pith/57NMV5ULYSENMA556BXFHT6RYM/bundle.json","state":"https://pith.science/pith/57NMV5ULYSENMA556BXFHT6RYM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/57NMV5ULYSENMA556BXFHT6RYM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:57NMV5ULYSENMA556BXFHT6RYM","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":"40c1d06e99d9fce73d2af9e3ccd25635f4f948845066275eeff796752de078af","cross_cats_sorted":["cs.AI","cs.CV","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-07-24T03:42:53Z","title_canon_sha256":"50539cb569078f22ef2e604a14fbc7c6d852c0279e64e257f9c832af10014e99"},"schema_version":"1.0","source":{"id":"1807.08894","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.08894","created_at":"2026-05-18T00:05:21Z"},{"alias_kind":"arxiv_version","alias_value":"1807.08894v2","created_at":"2026-05-18T00:05:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.08894","created_at":"2026-05-18T00:05:21Z"},{"alias_kind":"pith_short_12","alias_value":"57NMV5ULYSEN","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_16","alias_value":"57NMV5ULYSENMA55","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_8","alias_value":"57NMV5UL","created_at":"2026-05-18T12:32:08Z"}],"graph_snapshots":[{"event_id":"sha256:0c4b54a76b46a06b6b359f058c886dcd602186d934f45bf7f625e842e36f9397","target":"graph","created_at":"2026-05-18T00:05:21Z","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"},"paper":{"abstract_excerpt":"We propose a method for instance-level segmentation that uses RGB-D data as input and provides detailed information about the location, geometry and number of individual objects in the scene. This level of understanding is fundamental for autonomous robots. It enables safe and robust decision-making under the large uncertainty of the real-world. In our model, we propose to use the first and second order moments of the object occupancy function to represent an object instance. We train an hourglass Deep Neural Network (DNN) where each pixel in the output votes for the 3D position of the corresp","authors_text":"Jeannette Bohg, Lin Shao, Ye Tian","cross_cats":["cs.AI","cs.CV","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-07-24T03:42:53Z","title":"ClusterNet: 3D Instance Segmentation in RGB-D Images"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.08894","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":"sha256:4d28c4e698172b60d611e9f79d5fda73109fd9e842ee7cb69b79b98a21fd4257","target":"record","created_at":"2026-05-18T00:05:21Z","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":"40c1d06e99d9fce73d2af9e3ccd25635f4f948845066275eeff796752de078af","cross_cats_sorted":["cs.AI","cs.CV","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-07-24T03:42:53Z","title_canon_sha256":"50539cb569078f22ef2e604a14fbc7c6d852c0279e64e257f9c832af10014e99"},"schema_version":"1.0","source":{"id":"1807.08894","kind":"arxiv","version":2}},"canonical_sha256":"efdacaf68bc488d603bdf06e53cfd1c30f9f0267b7759cdcd39e8f6aa682c6c6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"efdacaf68bc488d603bdf06e53cfd1c30f9f0267b7759cdcd39e8f6aa682c6c6","first_computed_at":"2026-05-18T00:05:21.414666Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:05:21.414666Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"sm0066N3cgxSNlz6uSHQRlUeRtC2RVFaS7JLTTROghoU7MIuU3jpeCpKD3I1m+I6a/M0XVja/anlXIMnhTLBBg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:05:21.415099Z","signed_message":"canonical_sha256_bytes"},"source_id":"1807.08894","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4d28c4e698172b60d611e9f79d5fda73109fd9e842ee7cb69b79b98a21fd4257","sha256:0c4b54a76b46a06b6b359f058c886dcd602186d934f45bf7f625e842e36f9397"],"state_sha256":"7a42714abd8b8f443ac5c8118501508addb82a0f5d7a6aeb0f2a2bad7e6a46f6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mFQS44RkFqJ0EQ9qTsV8M1G9+C9wzaKvW61dokvThWS+HxE86xH98HePoblfrcTJc5MA45Y/9RYBx88x9ObiBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T20:59:47.411193Z","bundle_sha256":"f0cb1a12a26e4f883685caef3f37b7c22147ef264235f9888837d5fb3b389af9"}}