{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:DS3BK7XTFTAW5W2A6WATUGI2T4","short_pith_number":"pith:DS3BK7XT","schema_version":"1.0","canonical_sha256":"1cb6157ef32cc16edb40f5813a191a9f1ab527fd0a9adca84791c16a0e10aa9d","source":{"kind":"arxiv","id":"2007.12668","version":2},"attestation_state":"computed","paper":{"title":"KPRNet: Improving projection-based LiDAR semantic segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Deyvid Kochanov, Fatemeh Karimi Nejadasl, Olaf Booij","submitted_at":"2020-07-24T17:35:14Z","abstract_excerpt":"Semantic segmentation is an important component in the perception systems of autonomous vehicles. In this work, we adopt recent advances in both image and point cloud segmentation to achieve a better accuracy in the task of segmenting LiDAR scans. KPRNet improves the convolutional neural network architecture of 2D projection methods and utilizes KPConv to replace the commonly used post-processing techniques with a learnable point-wise component which allows us to obtain more accurate 3D labels. With these improvements our model outperforms the current best method on the SemanticKITTI benchmark"},"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":"2007.12668","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-07-24T17:35:14Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"d4b2d2a66e8ed70d9505d4b633d4a9c07d54428418fdb40f383f175f87b2f163","abstract_canon_sha256":"bbc2d0b6556b6dd6a94c790c1df0c729b267d9046df81378b680541181d4f70e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:28:51.818896Z","signature_b64":"/sbgQR7qQlyg09ph4G+PwbomgBadlnGSI8dqa8BIY0zj4pUWGXAoWO48sxHpHMR74wOfa3GyYlY4rmaOLj/1BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1cb6157ef32cc16edb40f5813a191a9f1ab527fd0a9adca84791c16a0e10aa9d","last_reissued_at":"2026-07-05T01:28:51.818335Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:28:51.818335Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"KPRNet: Improving projection-based LiDAR semantic segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Deyvid Kochanov, Fatemeh Karimi Nejadasl, Olaf Booij","submitted_at":"2020-07-24T17:35:14Z","abstract_excerpt":"Semantic segmentation is an important component in the perception systems of autonomous vehicles. In this work, we adopt recent advances in both image and point cloud segmentation to achieve a better accuracy in the task of segmenting LiDAR scans. KPRNet improves the convolutional neural network architecture of 2D projection methods and utilizes KPConv to replace the commonly used post-processing techniques with a learnable point-wise component which allows us to obtain more accurate 3D labels. With these improvements our model outperforms the current best method on the SemanticKITTI benchmark"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.12668","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/2007.12668/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":"2007.12668","created_at":"2026-07-05T01:28:51.818469+00:00"},{"alias_kind":"arxiv_version","alias_value":"2007.12668v2","created_at":"2026-07-05T01:28:51.818469+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.12668","created_at":"2026-07-05T01:28:51.818469+00:00"},{"alias_kind":"pith_short_12","alias_value":"DS3BK7XTFTAW","created_at":"2026-07-05T01:28:51.818469+00:00"},{"alias_kind":"pith_short_16","alias_value":"DS3BK7XTFTAW5W2A","created_at":"2026-07-05T01:28:51.818469+00:00"},{"alias_kind":"pith_short_8","alias_value":"DS3BK7XT","created_at":"2026-07-05T01:28:51.818469+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.31177","citing_title":"Vanilla ViT for Automotive Point Cloud Semantic Segmentation","ref_index":19,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DS3BK7XTFTAW5W2A6WATUGI2T4","json":"https://pith.science/pith/DS3BK7XTFTAW5W2A6WATUGI2T4.json","graph_json":"https://pith.science/api/pith-number/DS3BK7XTFTAW5W2A6WATUGI2T4/graph.json","events_json":"https://pith.science/api/pith-number/DS3BK7XTFTAW5W2A6WATUGI2T4/events.json","paper":"https://pith.science/paper/DS3BK7XT"},"agent_actions":{"view_html":"https://pith.science/pith/DS3BK7XTFTAW5W2A6WATUGI2T4","download_json":"https://pith.science/pith/DS3BK7XTFTAW5W2A6WATUGI2T4.json","view_paper":"https://pith.science/paper/DS3BK7XT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2007.12668&json=true","fetch_graph":"https://pith.science/api/pith-number/DS3BK7XTFTAW5W2A6WATUGI2T4/graph.json","fetch_events":"https://pith.science/api/pith-number/DS3BK7XTFTAW5W2A6WATUGI2T4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DS3BK7XTFTAW5W2A6WATUGI2T4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DS3BK7XTFTAW5W2A6WATUGI2T4/action/storage_attestation","attest_author":"https://pith.science/pith/DS3BK7XTFTAW5W2A6WATUGI2T4/action/author_attestation","sign_citation":"https://pith.science/pith/DS3BK7XTFTAW5W2A6WATUGI2T4/action/citation_signature","submit_replication":"https://pith.science/pith/DS3BK7XTFTAW5W2A6WATUGI2T4/action/replication_record"}},"created_at":"2026-07-05T01:28:51.818469+00:00","updated_at":"2026-07-05T01:28:51.818469+00:00"}