{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:ECIN7EL7BQL5GSQGE5PH3CUM5M","short_pith_number":"pith:ECIN7EL7","canonical_record":{"source":{"id":"2307.05219","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2023-07-11T12:44:06Z","cross_cats_sorted":[],"title_canon_sha256":"ab68220056526353a9c095632d28c9dc05b2387bd6a7fda7563a31a4b348a63c","abstract_canon_sha256":"bb01728fe82f118919ff432a627c390dc99165a6878503af5307c242432f448b"},"schema_version":"1.0"},"canonical_sha256":"2090df917f0c17d34a06275e7d8a8ceb2731b5f05c3175e4b036cf82b7786403","source":{"kind":"arxiv","id":"2307.05219","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.05219","created_at":"2026-07-05T09:51:28Z"},{"alias_kind":"arxiv_version","alias_value":"2307.05219v2","created_at":"2026-07-05T09:51:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.05219","created_at":"2026-07-05T09:51:28Z"},{"alias_kind":"pith_short_12","alias_value":"ECIN7EL7BQL5","created_at":"2026-07-05T09:51:28Z"},{"alias_kind":"pith_short_16","alias_value":"ECIN7EL7BQL5GSQG","created_at":"2026-07-05T09:51:28Z"},{"alias_kind":"pith_short_8","alias_value":"ECIN7EL7","created_at":"2026-07-05T09:51:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:ECIN7EL7BQL5GSQGE5PH3CUM5M","target":"record","payload":{"canonical_record":{"source":{"id":"2307.05219","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2023-07-11T12:44:06Z","cross_cats_sorted":[],"title_canon_sha256":"ab68220056526353a9c095632d28c9dc05b2387bd6a7fda7563a31a4b348a63c","abstract_canon_sha256":"bb01728fe82f118919ff432a627c390dc99165a6878503af5307c242432f448b"},"schema_version":"1.0"},"canonical_sha256":"2090df917f0c17d34a06275e7d8a8ceb2731b5f05c3175e4b036cf82b7786403","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:51:28.569400Z","signature_b64":"HtkxyJY5QOk0q9tngVcx7Ogw6UsB4Uk786vLE0ITMYAQyD+RCLJg1rHIzWyFxPCLDm41H90KILHWilw07+fWCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2090df917f0c17d34a06275e7d8a8ceb2731b5f05c3175e4b036cf82b7786403","last_reissued_at":"2026-07-05T09:51:28.568933Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:51:28.568933Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2307.05219","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":"2026-07-05T09:51:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oHm5dQaY8mHpWPXkQJxJBmh8kWs7mp8VzQu0G18WfeqCffZOzhyZ4Vm0st8F28J0in6LKS/5Ts78XVl49o96DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T06:48:17.462701Z"},"content_sha256":"aee812a9a26eac359fdd4bced0c3841a810b14ea531c7a89e491bf6bbefd7b58","schema_version":"1.0","event_id":"sha256:aee812a9a26eac359fdd4bced0c3841a810b14ea531c7a89e491bf6bbefd7b58"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:ECIN7EL7BQL5GSQGE5PH3CUM5M","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MinkSORT: A 3D deep feature extractor using sparse convolutions to improve 3D multi-object tracking in greenhouse tomato plants","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"David Rapado-Rincon, Eldert J. van Henten, Gert Kootstra","submitted_at":"2023-07-11T12:44:06Z","abstract_excerpt":"The agro-food industry is turning to robots to address the challenge of labour shortage. However, agro-food environments pose difficulties for robots due to high variation and occlusions. In the presence of these challenges, accurate world models, with information about object location, shape, and properties, are crucial for robots to perform tasks accurately. Building such models is challenging due to the complex and unique nature of agro-food environments, and errors in the model can lead to task execution issues. In this paper, MinkSORT, a novel method for generating tracking features using"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.05219","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/2307.05219/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":"2026-07-05T09:51:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3ghZG90IQK+OweqNyGylrUw7PZOI+aUhWHMKC6LBFm6HUdDjhAWsJFTx8Qwne5vS4dhi4uAq3l1al+sqLiLjDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T06:48:17.463073Z"},"content_sha256":"d4dde4be913ee19985f544fee70466ca0ac16b107b98a03c1ea2e1eec764f38f","schema_version":"1.0","event_id":"sha256:d4dde4be913ee19985f544fee70466ca0ac16b107b98a03c1ea2e1eec764f38f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ECIN7EL7BQL5GSQGE5PH3CUM5M/bundle.json","state_url":"https://pith.science/pith/ECIN7EL7BQL5GSQGE5PH3CUM5M/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ECIN7EL7BQL5GSQGE5PH3CUM5M/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":"2026-07-09T06:48:17Z","links":{"resolver":"https://pith.science/pith/ECIN7EL7BQL5GSQGE5PH3CUM5M","bundle":"https://pith.science/pith/ECIN7EL7BQL5GSQGE5PH3CUM5M/bundle.json","state":"https://pith.science/pith/ECIN7EL7BQL5GSQGE5PH3CUM5M/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ECIN7EL7BQL5GSQGE5PH3CUM5M/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:ECIN7EL7BQL5GSQGE5PH3CUM5M","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":"bb01728fe82f118919ff432a627c390dc99165a6878503af5307c242432f448b","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2023-07-11T12:44:06Z","title_canon_sha256":"ab68220056526353a9c095632d28c9dc05b2387bd6a7fda7563a31a4b348a63c"},"schema_version":"1.0","source":{"id":"2307.05219","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.05219","created_at":"2026-07-05T09:51:28Z"},{"alias_kind":"arxiv_version","alias_value":"2307.05219v2","created_at":"2026-07-05T09:51:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.05219","created_at":"2026-07-05T09:51:28Z"},{"alias_kind":"pith_short_12","alias_value":"ECIN7EL7BQL5","created_at":"2026-07-05T09:51:28Z"},{"alias_kind":"pith_short_16","alias_value":"ECIN7EL7BQL5GSQG","created_at":"2026-07-05T09:51:28Z"},{"alias_kind":"pith_short_8","alias_value":"ECIN7EL7","created_at":"2026-07-05T09:51:28Z"}],"graph_snapshots":[{"event_id":"sha256:d4dde4be913ee19985f544fee70466ca0ac16b107b98a03c1ea2e1eec764f38f","target":"graph","created_at":"2026-07-05T09:51:28Z","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/2307.05219/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The agro-food industry is turning to robots to address the challenge of labour shortage. However, agro-food environments pose difficulties for robots due to high variation and occlusions. In the presence of these challenges, accurate world models, with information about object location, shape, and properties, are crucial for robots to perform tasks accurately. Building such models is challenging due to the complex and unique nature of agro-food environments, and errors in the model can lead to task execution issues. In this paper, MinkSORT, a novel method for generating tracking features using","authors_text":"David Rapado-Rincon, Eldert J. van Henten, Gert Kootstra","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2023-07-11T12:44:06Z","title":"MinkSORT: A 3D deep feature extractor using sparse convolutions to improve 3D multi-object tracking in greenhouse tomato plants"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.05219","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:aee812a9a26eac359fdd4bced0c3841a810b14ea531c7a89e491bf6bbefd7b58","target":"record","created_at":"2026-07-05T09:51:28Z","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":"bb01728fe82f118919ff432a627c390dc99165a6878503af5307c242432f448b","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2023-07-11T12:44:06Z","title_canon_sha256":"ab68220056526353a9c095632d28c9dc05b2387bd6a7fda7563a31a4b348a63c"},"schema_version":"1.0","source":{"id":"2307.05219","kind":"arxiv","version":2}},"canonical_sha256":"2090df917f0c17d34a06275e7d8a8ceb2731b5f05c3175e4b036cf82b7786403","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2090df917f0c17d34a06275e7d8a8ceb2731b5f05c3175e4b036cf82b7786403","first_computed_at":"2026-07-05T09:51:28.568933Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:51:28.568933Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HtkxyJY5QOk0q9tngVcx7Ogw6UsB4Uk786vLE0ITMYAQyD+RCLJg1rHIzWyFxPCLDm41H90KILHWilw07+fWCA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:51:28.569400Z","signed_message":"canonical_sha256_bytes"},"source_id":"2307.05219","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:aee812a9a26eac359fdd4bced0c3841a810b14ea531c7a89e491bf6bbefd7b58","sha256:d4dde4be913ee19985f544fee70466ca0ac16b107b98a03c1ea2e1eec764f38f"],"state_sha256":"c56eafa94dbf62ce0931542d9ebd281cff457a072bcbee040e1954310097152b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"P57sZceFhrRy6cSNLkhJ3ALTI1OzwyH3f4H3fth2bTk6mOv4ZDZS3ns4YEG4/bRzYaH05ep+YxXpY4HPbW9NDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-09T06:48:17.465512Z","bundle_sha256":"c6d8f5f7332662ebd0774e5cf2a83c15a30fdf312d3bea0726747940c326654d"}}