{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:BGZKCXN7SAHEEDJXESRLU4BGHC","short_pith_number":"pith:BGZKCXN7","schema_version":"1.0","canonical_sha256":"09b2a15dbf900e420d3724a2ba702638b1cf98e414b644ea3f94a7b552527a59","source":{"kind":"arxiv","id":"2410.14790","version":2},"attestation_state":"computed","paper":{"title":"SSL-NBV: A Self-Supervised-Learning-Based Next-Best-View algorithm for Efficient 3D Plant Reconstruction by a Robot","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Akshay K. Burusa, Eldert J. van Henten, Gert Kootstra, Jianchao Ci, Xin Wang","submitted_at":"2024-10-02T13:42:38Z","abstract_excerpt":"The 3D reconstruction of plants is challenging due to their complex shape causing many occlusions. Next-Best-View (NBV) methods address this by iteratively selecting new viewpoints to maximize information gain (IG). Deep-learning-based NBV (DL-NBV) methods demonstrate higher computational efficiency over classic voxel-based NBV approaches but current methods require extensive training using ground-truth plant models, making them impractical for real-world plants. These methods, moreover, rely on offline training with pre-collected data, limiting adaptability in changing agricultural environmen"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2410.14790","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-10-02T13:42:38Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"e95d65ab178cb972ed092dae3e615e50bbc794df63afb24680a16c70d7792393","abstract_canon_sha256":"c2d94dd03dfc46c82814794cdaf107d1866ee7065603cb9b8698a904a6a235d0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:28:56.383705Z","signature_b64":"9aWGE/NW522q3q2LwGAXzOR/FC2/yMWUo2luDXxvoTRP3N0SGZ4lKWaCPpzm6o8OIvvXsjdFrb1MOEzxpwUtAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"09b2a15dbf900e420d3724a2ba702638b1cf98e414b644ea3f94a7b552527a59","last_reissued_at":"2026-07-05T09:28:56.383216Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:28:56.383216Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SSL-NBV: A Self-Supervised-Learning-Based Next-Best-View algorithm for Efficient 3D Plant Reconstruction by a Robot","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Akshay K. Burusa, Eldert J. van Henten, Gert Kootstra, Jianchao Ci, Xin Wang","submitted_at":"2024-10-02T13:42:38Z","abstract_excerpt":"The 3D reconstruction of plants is challenging due to their complex shape causing many occlusions. Next-Best-View (NBV) methods address this by iteratively selecting new viewpoints to maximize information gain (IG). Deep-learning-based NBV (DL-NBV) methods demonstrate higher computational efficiency over classic voxel-based NBV approaches but current methods require extensive training using ground-truth plant models, making them impractical for real-world plants. These methods, moreover, rely on offline training with pre-collected data, limiting adaptability in changing agricultural environmen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.14790","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/2410.14790/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2410.14790","created_at":"2026-07-05T09:28:56.383274+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.14790v2","created_at":"2026-07-05T09:28:56.383274+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.14790","created_at":"2026-07-05T09:28:56.383274+00:00"},{"alias_kind":"pith_short_12","alias_value":"BGZKCXN7SAHE","created_at":"2026-07-05T09:28:56.383274+00:00"},{"alias_kind":"pith_short_16","alias_value":"BGZKCXN7SAHEEDJX","created_at":"2026-07-05T09:28:56.383274+00:00"},{"alias_kind":"pith_short_8","alias_value":"BGZKCXN7","created_at":"2026-07-05T09:28:56.383274+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BGZKCXN7SAHEEDJXESRLU4BGHC","json":"https://pith.science/pith/BGZKCXN7SAHEEDJXESRLU4BGHC.json","graph_json":"https://pith.science/api/pith-number/BGZKCXN7SAHEEDJXESRLU4BGHC/graph.json","events_json":"https://pith.science/api/pith-number/BGZKCXN7SAHEEDJXESRLU4BGHC/events.json","paper":"https://pith.science/paper/BGZKCXN7"},"agent_actions":{"view_html":"https://pith.science/pith/BGZKCXN7SAHEEDJXESRLU4BGHC","download_json":"https://pith.science/pith/BGZKCXN7SAHEEDJXESRLU4BGHC.json","view_paper":"https://pith.science/paper/BGZKCXN7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.14790&json=true","fetch_graph":"https://pith.science/api/pith-number/BGZKCXN7SAHEEDJXESRLU4BGHC/graph.json","fetch_events":"https://pith.science/api/pith-number/BGZKCXN7SAHEEDJXESRLU4BGHC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BGZKCXN7SAHEEDJXESRLU4BGHC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BGZKCXN7SAHEEDJXESRLU4BGHC/action/storage_attestation","attest_author":"https://pith.science/pith/BGZKCXN7SAHEEDJXESRLU4BGHC/action/author_attestation","sign_citation":"https://pith.science/pith/BGZKCXN7SAHEEDJXESRLU4BGHC/action/citation_signature","submit_replication":"https://pith.science/pith/BGZKCXN7SAHEEDJXESRLU4BGHC/action/replication_record"}},"created_at":"2026-07-05T09:28:56.383274+00:00","updated_at":"2026-07-05T09:28:56.383274+00:00"}