{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:SABZTYPHDXA6T4QIUTKMDLB3PY","short_pith_number":"pith:SABZTYPH","schema_version":"1.0","canonical_sha256":"900399e1e71dc1e9f208a4d4c1ac3b7e1fc6ed35bf7a1eb97d4f7d3c99c5f506","source":{"kind":"arxiv","id":"2508.00900","version":1},"attestation_state":"computed","paper":{"title":"Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.RO","authors_text":"Mohsen Soryani, Sina Mansouri, Taha Samavati","submitted_at":"2025-07-28T16:09:34Z","abstract_excerpt":"The global demand for medicinal plants, such as Damask roses, has surged with population growth, yet labor-intensive harvesting remains a bottleneck for scalability. To address this, we propose a novel 3D perception pipeline tailored for flower-harvesting robots, focusing on sparse 3D localization of rose centers. Our two-stage algorithm first performs 2D point-based detection on stereo images, followed by depth estimation using a lightweight deep neural network. To overcome the challenge of scarce real-world labeled data, we introduce a photorealistic synthetic dataset generated via Blender, "},"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":"2508.00900","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.RO","submitted_at":"2025-07-28T16:09:34Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"2d71329694f6311c82e63537f8c0092219a2bfe84980cbcfc8a85fc331d15c67","abstract_canon_sha256":"6de71efac5738d9ae410b6fff4b4dec96f0450a6f0415ef7f52805ae374ae125"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:47:29.491558Z","signature_b64":"eDszf7eEEkCYBq004bsRPLYDsa7xUcQyTs4PFQA0y1Rb50tmuOKNwWmaxncJzBj43oHbBW+3EwOMfGeALFBDBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"900399e1e71dc1e9f208a4d4c1ac3b7e1fc6ed35bf7a1eb97d4f7d3c99c5f506","last_reissued_at":"2026-07-05T11:47:29.491210Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:47:29.491210Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.RO","authors_text":"Mohsen Soryani, Sina Mansouri, Taha Samavati","submitted_at":"2025-07-28T16:09:34Z","abstract_excerpt":"The global demand for medicinal plants, such as Damask roses, has surged with population growth, yet labor-intensive harvesting remains a bottleneck for scalability. To address this, we propose a novel 3D perception pipeline tailored for flower-harvesting robots, focusing on sparse 3D localization of rose centers. Our two-stage algorithm first performs 2D point-based detection on stereo images, followed by depth estimation using a lightweight deep neural network. To overcome the challenge of scarce real-world labeled data, we introduce a photorealistic synthetic dataset generated via Blender, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.00900","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/2508.00900/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":"2508.00900","created_at":"2026-07-05T11:47:29.491263+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.00900v1","created_at":"2026-07-05T11:47:29.491263+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.00900","created_at":"2026-07-05T11:47:29.491263+00:00"},{"alias_kind":"pith_short_12","alias_value":"SABZTYPHDXA6","created_at":"2026-07-05T11:47:29.491263+00:00"},{"alias_kind":"pith_short_16","alias_value":"SABZTYPHDXA6T4QI","created_at":"2026-07-05T11:47:29.491263+00:00"},{"alias_kind":"pith_short_8","alias_value":"SABZTYPH","created_at":"2026-07-05T11:47:29.491263+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/SABZTYPHDXA6T4QIUTKMDLB3PY","json":"https://pith.science/pith/SABZTYPHDXA6T4QIUTKMDLB3PY.json","graph_json":"https://pith.science/api/pith-number/SABZTYPHDXA6T4QIUTKMDLB3PY/graph.json","events_json":"https://pith.science/api/pith-number/SABZTYPHDXA6T4QIUTKMDLB3PY/events.json","paper":"https://pith.science/paper/SABZTYPH"},"agent_actions":{"view_html":"https://pith.science/pith/SABZTYPHDXA6T4QIUTKMDLB3PY","download_json":"https://pith.science/pith/SABZTYPHDXA6T4QIUTKMDLB3PY.json","view_paper":"https://pith.science/paper/SABZTYPH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.00900&json=true","fetch_graph":"https://pith.science/api/pith-number/SABZTYPHDXA6T4QIUTKMDLB3PY/graph.json","fetch_events":"https://pith.science/api/pith-number/SABZTYPHDXA6T4QIUTKMDLB3PY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SABZTYPHDXA6T4QIUTKMDLB3PY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SABZTYPHDXA6T4QIUTKMDLB3PY/action/storage_attestation","attest_author":"https://pith.science/pith/SABZTYPHDXA6T4QIUTKMDLB3PY/action/author_attestation","sign_citation":"https://pith.science/pith/SABZTYPHDXA6T4QIUTKMDLB3PY/action/citation_signature","submit_replication":"https://pith.science/pith/SABZTYPHDXA6T4QIUTKMDLB3PY/action/replication_record"}},"created_at":"2026-07-05T11:47:29.491263+00:00","updated_at":"2026-07-05T11:47:29.491263+00:00"}