{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:OMAHRS7OFZCFCZKISM53EV67CW","short_pith_number":"pith:OMAHRS7O","schema_version":"1.0","canonical_sha256":"730078cbee2e44516548933bb257df15a488dce8df0e9d28435c6d6a8ae55375","source":{"kind":"arxiv","id":"1704.03847","version":1},"attestation_state":"computed","paper":{"title":"Semantic3D.net: A new Large-scale Point Cloud Classification Benchmark","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NE","cs.RO"],"primary_cat":"cs.CV","authors_text":"Jan D. Wegner, Konrad Schindler, Lubor Ladicky, Marc Pollefeys, Nikolay Savinov, Timo Hackel","submitted_at":"2017-04-12T17:12:57Z","abstract_excerpt":"This paper presents a new 3D point cloud classification benchmark data set with over four billion manually labelled points, meant as input for data-hungry (deep) learning methods. We also discuss first submissions to the benchmark that use deep convolutional neural networks (CNNs) as a work horse, which already show remarkable performance improvements over state-of-the-art. CNNs have become the de-facto standard for many tasks in computer vision and machine learning like semantic segmentation or object detection in images, but have no yet led to a true breakthrough for 3D point cloud labelling"},"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":"1704.03847","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-04-12T17:12:57Z","cross_cats_sorted":["cs.LG","cs.NE","cs.RO"],"title_canon_sha256":"cc7e52e53f30919ec593038869e2dc059df52e46d78e72658c5160cad8b6b102","abstract_canon_sha256":"20701a2f2392fe2d300010ec5a9a34eae751c6efb43918148a58fe29d42e9269"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:46:26.732034Z","signature_b64":"b42OqrViZ5Igp1EAtBS+NSgwO1Tk2ulxBwG0pcY36Ov670DcsZGYlC6DWRLIefHFXIUlHeYJSu0BjnxORUYvAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"730078cbee2e44516548933bb257df15a488dce8df0e9d28435c6d6a8ae55375","last_reissued_at":"2026-05-18T00:46:26.731294Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:46:26.731294Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Semantic3D.net: A new Large-scale Point Cloud Classification Benchmark","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NE","cs.RO"],"primary_cat":"cs.CV","authors_text":"Jan D. Wegner, Konrad Schindler, Lubor Ladicky, Marc Pollefeys, Nikolay Savinov, Timo Hackel","submitted_at":"2017-04-12T17:12:57Z","abstract_excerpt":"This paper presents a new 3D point cloud classification benchmark data set with over four billion manually labelled points, meant as input for data-hungry (deep) learning methods. We also discuss first submissions to the benchmark that use deep convolutional neural networks (CNNs) as a work horse, which already show remarkable performance improvements over state-of-the-art. CNNs have become the de-facto standard for many tasks in computer vision and machine learning like semantic segmentation or object detection in images, but have no yet led to a true breakthrough for 3D point cloud labelling"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.03847","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":""},"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":"1704.03847","created_at":"2026-05-18T00:46:26.731427+00:00"},{"alias_kind":"arxiv_version","alias_value":"1704.03847v1","created_at":"2026-05-18T00:46:26.731427+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.03847","created_at":"2026-05-18T00:46:26.731427+00:00"},{"alias_kind":"pith_short_12","alias_value":"OMAHRS7OFZCF","created_at":"2026-05-18T12:31:34.259226+00:00"},{"alias_kind":"pith_short_16","alias_value":"OMAHRS7OFZCFCZKI","created_at":"2026-05-18T12:31:34.259226+00:00"},{"alias_kind":"pith_short_8","alias_value":"OMAHRS7O","created_at":"2026-05-18T12:31:34.259226+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2510.06687","citing_title":"Geometry-Aware Cross Modal Alignment for Light Field-LiDAR Semantic Segmentation","ref_index":34,"is_internal_anchor":true},{"citing_arxiv_id":"2604.02497","citing_title":"Delaunay Canopy: Building Wireframe Reconstruction from Airborne LiDAR Point Clouds via Delaunay Graph","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2604.22354","citing_title":"One Shot Learning for Edge Detection on Point Clouds","ref_index":5,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OMAHRS7OFZCFCZKISM53EV67CW","json":"https://pith.science/pith/OMAHRS7OFZCFCZKISM53EV67CW.json","graph_json":"https://pith.science/api/pith-number/OMAHRS7OFZCFCZKISM53EV67CW/graph.json","events_json":"https://pith.science/api/pith-number/OMAHRS7OFZCFCZKISM53EV67CW/events.json","paper":"https://pith.science/paper/OMAHRS7O"},"agent_actions":{"view_html":"https://pith.science/pith/OMAHRS7OFZCFCZKISM53EV67CW","download_json":"https://pith.science/pith/OMAHRS7OFZCFCZKISM53EV67CW.json","view_paper":"https://pith.science/paper/OMAHRS7O","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1704.03847&json=true","fetch_graph":"https://pith.science/api/pith-number/OMAHRS7OFZCFCZKISM53EV67CW/graph.json","fetch_events":"https://pith.science/api/pith-number/OMAHRS7OFZCFCZKISM53EV67CW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OMAHRS7OFZCFCZKISM53EV67CW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OMAHRS7OFZCFCZKISM53EV67CW/action/storage_attestation","attest_author":"https://pith.science/pith/OMAHRS7OFZCFCZKISM53EV67CW/action/author_attestation","sign_citation":"https://pith.science/pith/OMAHRS7OFZCFCZKISM53EV67CW/action/citation_signature","submit_replication":"https://pith.science/pith/OMAHRS7OFZCFCZKISM53EV67CW/action/replication_record"}},"created_at":"2026-05-18T00:46:26.731427+00:00","updated_at":"2026-05-18T00:46:26.731427+00:00"}