{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:N5V7N5ZPJ5ASZ7BDFCHZ3DLXND","short_pith_number":"pith:N5V7N5ZP","schema_version":"1.0","canonical_sha256":"6f6bf6f72f4f412cfc23288f9d8d7768fa42aa037a24aced055856e7ab078aa5","source":{"kind":"arxiv","id":"2606.01549","version":1},"attestation_state":"computed","paper":{"title":"ForestMamba: Sparse Mamba with Geometry-guided Queries for 3D Forest Point Cloud Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Duc Viet Le, Ichiro Ide, Takahiro Komamizu, Teja Kattenborn, Trung Thanh Nguyen, Tuan-Anh Vu, Yasutomo Kawanishi","submitted_at":"2026-06-01T01:49:09Z","abstract_excerpt":"AI-based semantic and instance segmentation of terrestrial and drone LiDAR point clouds is emerging as a transformative approach for converting the complex 3D structure of forests into actionable information for forest monitoring and biodiversity assessment. However, forest LiDAR scenes remain highly challenging due to their large data volumes, irregular sampling density, overlapping and complex canopy structure, and geographic variability. Existing methods based on sparse convolutions or Transformers achieve promising results, but suffer from two key limitations: Quadratic complexity of atten"},"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":"2606.01549","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-01T01:49:09Z","cross_cats_sorted":[],"title_canon_sha256":"b2d1baa2a14f2e1a5763d771f28849626abb48a90d331a8616d35ee767812830","abstract_canon_sha256":"e34c4c61411b9a66d78bb7d4f3f761df77fb96efd740dd6626bfb22048b1fede"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-02T02:04:36.099066Z","signature_b64":"Q4lubDpTFpv7dvtLeCk5ceJ4y5s7/6GL9vhnjirkcwq99WwNaH1KYJUjZfj4kfgwcyFHZc4ie8p/bghe6fGvDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6f6bf6f72f4f412cfc23288f9d8d7768fa42aa037a24aced055856e7ab078aa5","last_reissued_at":"2026-06-02T02:04:36.098647Z","signature_status":"signed_v1","first_computed_at":"2026-06-02T02:04:36.098647Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ForestMamba: Sparse Mamba with Geometry-guided Queries for 3D Forest Point Cloud Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Duc Viet Le, Ichiro Ide, Takahiro Komamizu, Teja Kattenborn, Trung Thanh Nguyen, Tuan-Anh Vu, Yasutomo Kawanishi","submitted_at":"2026-06-01T01:49:09Z","abstract_excerpt":"AI-based semantic and instance segmentation of terrestrial and drone LiDAR point clouds is emerging as a transformative approach for converting the complex 3D structure of forests into actionable information for forest monitoring and biodiversity assessment. However, forest LiDAR scenes remain highly challenging due to their large data volumes, irregular sampling density, overlapping and complex canopy structure, and geographic variability. Existing methods based on sparse convolutions or Transformers achieve promising results, but suffer from two key limitations: Quadratic complexity of atten"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.01549","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/2606.01549/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":"2606.01549","created_at":"2026-06-02T02:04:36.098717+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.01549v1","created_at":"2026-06-02T02:04:36.098717+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.01549","created_at":"2026-06-02T02:04:36.098717+00:00"},{"alias_kind":"pith_short_12","alias_value":"N5V7N5ZPJ5AS","created_at":"2026-06-02T02:04:36.098717+00:00"},{"alias_kind":"pith_short_16","alias_value":"N5V7N5ZPJ5ASZ7BD","created_at":"2026-06-02T02:04:36.098717+00:00"},{"alias_kind":"pith_short_8","alias_value":"N5V7N5ZP","created_at":"2026-06-02T02:04:36.098717+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/N5V7N5ZPJ5ASZ7BDFCHZ3DLXND","json":"https://pith.science/pith/N5V7N5ZPJ5ASZ7BDFCHZ3DLXND.json","graph_json":"https://pith.science/api/pith-number/N5V7N5ZPJ5ASZ7BDFCHZ3DLXND/graph.json","events_json":"https://pith.science/api/pith-number/N5V7N5ZPJ5ASZ7BDFCHZ3DLXND/events.json","paper":"https://pith.science/paper/N5V7N5ZP"},"agent_actions":{"view_html":"https://pith.science/pith/N5V7N5ZPJ5ASZ7BDFCHZ3DLXND","download_json":"https://pith.science/pith/N5V7N5ZPJ5ASZ7BDFCHZ3DLXND.json","view_paper":"https://pith.science/paper/N5V7N5ZP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.01549&json=true","fetch_graph":"https://pith.science/api/pith-number/N5V7N5ZPJ5ASZ7BDFCHZ3DLXND/graph.json","fetch_events":"https://pith.science/api/pith-number/N5V7N5ZPJ5ASZ7BDFCHZ3DLXND/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/N5V7N5ZPJ5ASZ7BDFCHZ3DLXND/action/timestamp_anchor","attest_storage":"https://pith.science/pith/N5V7N5ZPJ5ASZ7BDFCHZ3DLXND/action/storage_attestation","attest_author":"https://pith.science/pith/N5V7N5ZPJ5ASZ7BDFCHZ3DLXND/action/author_attestation","sign_citation":"https://pith.science/pith/N5V7N5ZPJ5ASZ7BDFCHZ3DLXND/action/citation_signature","submit_replication":"https://pith.science/pith/N5V7N5ZPJ5ASZ7BDFCHZ3DLXND/action/replication_record"}},"created_at":"2026-06-02T02:04:36.098717+00:00","updated_at":"2026-06-02T02:04:36.098717+00:00"}