{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:BTBTNXWSY2Y2B4J3IAMQGPTSBE","short_pith_number":"pith:BTBTNXWS","schema_version":"1.0","canonical_sha256":"0cc336ded2c6b1a0f13b4019033e72091cd5f8748cd4449d6474a568a1c2ce68","source":{"kind":"arxiv","id":"2606.09111","version":1},"attestation_state":"computed","paper":{"title":"Illumination-Invariant Anomaly Detection for Sub-Canopy UAV Multispectral Point Clouds","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Likun Chen, Xian Li, Yanfeng Gu","submitted_at":"2026-06-08T07:04:26Z","abstract_excerpt":"Unmanned Aerial Vehicle (UAV) multispectral point clouds (MPC) provide high-dimensional spatial-spectral data for sub-canopy target detection; however, their efficacy is significantly compromised by severe illumination heterogeneity caused by vegetation shadows. To address this, we propose a prior-free anomaly detection framework capable of robustly handling lighting variations. First, we formulate solar angle estimation as an inverse optimization problem. By coupling spectral indices with a ray-tracing model, this strategy achieves Prior-Free Shadow Extraction without relying on flight metada"},"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":"2606.09111","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-08T07:04:26Z","cross_cats_sorted":[],"title_canon_sha256":"34ec5b46bdb7330da17ec4f4d36c5f1221422f611befdc0aa2c60adca7da8fac","abstract_canon_sha256":"8e0f8d799cec3b3701d97ce753fee353ed5cd0001f69368c60c692d1fd56bf32"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-09T02:07:59.693655Z","signature_b64":"nRJ0iW3x+S6OJ2lPyfn3U6KjG5kft2yGzGYZhjzXkekzd8+p+Tw7DqrzAzoWh5tiIEogAFvL9D3EEzmfp9iHAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0cc336ded2c6b1a0f13b4019033e72091cd5f8748cd4449d6474a568a1c2ce68","last_reissued_at":"2026-06-09T02:07:59.692889Z","signature_status":"signed_v1","first_computed_at":"2026-06-09T02:07:59.692889Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Illumination-Invariant Anomaly Detection for Sub-Canopy UAV Multispectral Point Clouds","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Likun Chen, Xian Li, Yanfeng Gu","submitted_at":"2026-06-08T07:04:26Z","abstract_excerpt":"Unmanned Aerial Vehicle (UAV) multispectral point clouds (MPC) provide high-dimensional spatial-spectral data for sub-canopy target detection; however, their efficacy is significantly compromised by severe illumination heterogeneity caused by vegetation shadows. To address this, we propose a prior-free anomaly detection framework capable of robustly handling lighting variations. First, we formulate solar angle estimation as an inverse optimization problem. By coupling spectral indices with a ray-tracing model, this strategy achieves Prior-Free Shadow Extraction without relying on flight metada"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.09111","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/2606.09111/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":"2606.09111","created_at":"2026-06-09T02:07:59.693009+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.09111v1","created_at":"2026-06-09T02:07:59.693009+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.09111","created_at":"2026-06-09T02:07:59.693009+00:00"},{"alias_kind":"pith_short_12","alias_value":"BTBTNXWSY2Y2","created_at":"2026-06-09T02:07:59.693009+00:00"},{"alias_kind":"pith_short_16","alias_value":"BTBTNXWSY2Y2B4J3","created_at":"2026-06-09T02:07:59.693009+00:00"},{"alias_kind":"pith_short_8","alias_value":"BTBTNXWS","created_at":"2026-06-09T02:07:59.693009+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/BTBTNXWSY2Y2B4J3IAMQGPTSBE","json":"https://pith.science/pith/BTBTNXWSY2Y2B4J3IAMQGPTSBE.json","graph_json":"https://pith.science/api/pith-number/BTBTNXWSY2Y2B4J3IAMQGPTSBE/graph.json","events_json":"https://pith.science/api/pith-number/BTBTNXWSY2Y2B4J3IAMQGPTSBE/events.json","paper":"https://pith.science/paper/BTBTNXWS"},"agent_actions":{"view_html":"https://pith.science/pith/BTBTNXWSY2Y2B4J3IAMQGPTSBE","download_json":"https://pith.science/pith/BTBTNXWSY2Y2B4J3IAMQGPTSBE.json","view_paper":"https://pith.science/paper/BTBTNXWS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.09111&json=true","fetch_graph":"https://pith.science/api/pith-number/BTBTNXWSY2Y2B4J3IAMQGPTSBE/graph.json","fetch_events":"https://pith.science/api/pith-number/BTBTNXWSY2Y2B4J3IAMQGPTSBE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BTBTNXWSY2Y2B4J3IAMQGPTSBE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BTBTNXWSY2Y2B4J3IAMQGPTSBE/action/storage_attestation","attest_author":"https://pith.science/pith/BTBTNXWSY2Y2B4J3IAMQGPTSBE/action/author_attestation","sign_citation":"https://pith.science/pith/BTBTNXWSY2Y2B4J3IAMQGPTSBE/action/citation_signature","submit_replication":"https://pith.science/pith/BTBTNXWSY2Y2B4J3IAMQGPTSBE/action/replication_record"}},"created_at":"2026-06-09T02:07:59.693009+00:00","updated_at":"2026-06-09T02:07:59.693009+00:00"}