{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ITXOX7Z3QPXT24KGERIMYBUFXP","short_pith_number":"pith:ITXOX7Z3","schema_version":"1.0","canonical_sha256":"44eeebff3b83ef3d71462450cc0685bbddaf7e7aef051993709364e9aad6e1b9","source":{"kind":"arxiv","id":"2401.01439","version":1},"attestation_state":"computed","paper":{"title":"Off-Road LiDAR Intensity Based Semantic Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Kasi Viswanath, Peng Jiang, Srikanth Saripalli, Sujit PB","submitted_at":"2024-01-02T21:27:43Z","abstract_excerpt":"LiDAR is used in autonomous driving to provide 3D spatial information and enable accurate perception in off-road environments, aiding in obstacle detection, mapping, and path planning. Learning-based LiDAR semantic segmentation utilizes machine learning techniques to automatically classify objects and regions in LiDAR point clouds. Learning-based models struggle in off-road environments due to the presence of diverse objects with varying colors, textures, and undefined boundaries, which can lead to difficulties in accurately classifying and segmenting objects using traditional geometric-based "},"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":"2401.01439","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-01-02T21:27:43Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"0ba8232d5f9245949b96455b93a60ed23c6985b68cf1632ca0d181ba2086947f","abstract_canon_sha256":"d8b5730b960067507c1f3ff2354a919dcffa7432323b6c192cbfbf204cc07364"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:00:56.980135Z","signature_b64":"9/xJUTFLt1naqeODKHHmrLyv2UNGtfBbvPOHEz4gVBExzYJPwAMAHIK3KtNsuJT3Fhut+eGJyM+4biFsmlUFBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"44eeebff3b83ef3d71462450cc0685bbddaf7e7aef051993709364e9aad6e1b9","last_reissued_at":"2026-07-05T09:00:56.979671Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:00:56.979671Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Off-Road LiDAR Intensity Based Semantic Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Kasi Viswanath, Peng Jiang, Srikanth Saripalli, Sujit PB","submitted_at":"2024-01-02T21:27:43Z","abstract_excerpt":"LiDAR is used in autonomous driving to provide 3D spatial information and enable accurate perception in off-road environments, aiding in obstacle detection, mapping, and path planning. Learning-based LiDAR semantic segmentation utilizes machine learning techniques to automatically classify objects and regions in LiDAR point clouds. Learning-based models struggle in off-road environments due to the presence of diverse objects with varying colors, textures, and undefined boundaries, which can lead to difficulties in accurately classifying and segmenting objects using traditional geometric-based "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.01439","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/2401.01439/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":"2401.01439","created_at":"2026-07-05T09:00:56.979729+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.01439v1","created_at":"2026-07-05T09:00:56.979729+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.01439","created_at":"2026-07-05T09:00:56.979729+00:00"},{"alias_kind":"pith_short_12","alias_value":"ITXOX7Z3QPXT","created_at":"2026-07-05T09:00:56.979729+00:00"},{"alias_kind":"pith_short_16","alias_value":"ITXOX7Z3QPXT24KG","created_at":"2026-07-05T09:00:56.979729+00:00"},{"alias_kind":"pith_short_8","alias_value":"ITXOX7Z3","created_at":"2026-07-05T09:00:56.979729+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/ITXOX7Z3QPXT24KGERIMYBUFXP","json":"https://pith.science/pith/ITXOX7Z3QPXT24KGERIMYBUFXP.json","graph_json":"https://pith.science/api/pith-number/ITXOX7Z3QPXT24KGERIMYBUFXP/graph.json","events_json":"https://pith.science/api/pith-number/ITXOX7Z3QPXT24KGERIMYBUFXP/events.json","paper":"https://pith.science/paper/ITXOX7Z3"},"agent_actions":{"view_html":"https://pith.science/pith/ITXOX7Z3QPXT24KGERIMYBUFXP","download_json":"https://pith.science/pith/ITXOX7Z3QPXT24KGERIMYBUFXP.json","view_paper":"https://pith.science/paper/ITXOX7Z3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.01439&json=true","fetch_graph":"https://pith.science/api/pith-number/ITXOX7Z3QPXT24KGERIMYBUFXP/graph.json","fetch_events":"https://pith.science/api/pith-number/ITXOX7Z3QPXT24KGERIMYBUFXP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ITXOX7Z3QPXT24KGERIMYBUFXP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ITXOX7Z3QPXT24KGERIMYBUFXP/action/storage_attestation","attest_author":"https://pith.science/pith/ITXOX7Z3QPXT24KGERIMYBUFXP/action/author_attestation","sign_citation":"https://pith.science/pith/ITXOX7Z3QPXT24KGERIMYBUFXP/action/citation_signature","submit_replication":"https://pith.science/pith/ITXOX7Z3QPXT24KGERIMYBUFXP/action/replication_record"}},"created_at":"2026-07-05T09:00:56.979729+00:00","updated_at":"2026-07-05T09:00:56.979729+00:00"}