{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:CIAKGNSOTQSIWUY2VKBO7A7FWK","short_pith_number":"pith:CIAKGNSO","schema_version":"1.0","canonical_sha256":"1200a3364e9c248b531aaa82ef83e5b29bddd7faa89a6cfdd3a080babca71ed7","source":{"kind":"arxiv","id":"1902.04997","version":3},"attestation_state":"computed","paper":{"title":"Gated2Depth: Real-time Dense Lidar from Gated Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Felix Heide, Frank Julca-Aguilar, Klaus Dietmayer, Mario Bijelic, Tobias Gruber, Werner Ritter","submitted_at":"2019-02-13T16:41:52Z","abstract_excerpt":"We present an imaging framework which converts three images from a gated camera into high-resolution depth maps with depth accuracy comparable to pulsed lidar measurements. Existing scanning lidar systems achieve low spatial resolution at large ranges due to mechanically-limited angular sampling rates, restricting scene understanding tasks to close-range clusters with dense sampling. Moreover, today's pulsed lidar scanners suffer from high cost, power consumption, large form-factors, and they fail in the presence of strong backscatter. We depart from point scanning and demonstrate that it is p"},"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":"1902.04997","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-02-13T16:41:52Z","cross_cats_sorted":[],"title_canon_sha256":"76c80f402d4665f56cf87a37cff412a72dc56e8720d9ad232e2027a9b5af62e2","abstract_canon_sha256":"6496fb74ea2b9fb0c14931afcb28fa38cdba64374e84dddd1e30dde341222455"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:15:02.911718Z","signature_b64":"8ADDyZHeNOlMwuv8Ui3YvXBDi8UThVSD77S6zlapMcIxz8FOToB9JqNcoSmj7howfpGhzpYnxOFKHJNjTfTHDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1200a3364e9c248b531aaa82ef83e5b29bddd7faa89a6cfdd3a080babca71ed7","last_reissued_at":"2026-07-05T00:15:02.911194Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:15:02.911194Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Gated2Depth: Real-time Dense Lidar from Gated Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Felix Heide, Frank Julca-Aguilar, Klaus Dietmayer, Mario Bijelic, Tobias Gruber, Werner Ritter","submitted_at":"2019-02-13T16:41:52Z","abstract_excerpt":"We present an imaging framework which converts three images from a gated camera into high-resolution depth maps with depth accuracy comparable to pulsed lidar measurements. Existing scanning lidar systems achieve low spatial resolution at large ranges due to mechanically-limited angular sampling rates, restricting scene understanding tasks to close-range clusters with dense sampling. Moreover, today's pulsed lidar scanners suffer from high cost, power consumption, large form-factors, and they fail in the presence of strong backscatter. We depart from point scanning and demonstrate that it is p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.04997","kind":"arxiv","version":3},"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/1902.04997/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":"1902.04997","created_at":"2026-07-05T00:15:02.911250+00:00"},{"alias_kind":"arxiv_version","alias_value":"1902.04997v3","created_at":"2026-07-05T00:15:02.911250+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.04997","created_at":"2026-07-05T00:15:02.911250+00:00"},{"alias_kind":"pith_short_12","alias_value":"CIAKGNSOTQSI","created_at":"2026-07-05T00:15:02.911250+00:00"},{"alias_kind":"pith_short_16","alias_value":"CIAKGNSOTQSIWUY2","created_at":"2026-07-05T00:15:02.911250+00:00"},{"alias_kind":"pith_short_8","alias_value":"CIAKGNSO","created_at":"2026-07-05T00:15:02.911250+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.02052","citing_title":"FoveaSPAD: Exploiting Depth Priors for Adaptive and Efficient Single-Photon 3D Imaging","ref_index":28,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CIAKGNSOTQSIWUY2VKBO7A7FWK","json":"https://pith.science/pith/CIAKGNSOTQSIWUY2VKBO7A7FWK.json","graph_json":"https://pith.science/api/pith-number/CIAKGNSOTQSIWUY2VKBO7A7FWK/graph.json","events_json":"https://pith.science/api/pith-number/CIAKGNSOTQSIWUY2VKBO7A7FWK/events.json","paper":"https://pith.science/paper/CIAKGNSO"},"agent_actions":{"view_html":"https://pith.science/pith/CIAKGNSOTQSIWUY2VKBO7A7FWK","download_json":"https://pith.science/pith/CIAKGNSOTQSIWUY2VKBO7A7FWK.json","view_paper":"https://pith.science/paper/CIAKGNSO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1902.04997&json=true","fetch_graph":"https://pith.science/api/pith-number/CIAKGNSOTQSIWUY2VKBO7A7FWK/graph.json","fetch_events":"https://pith.science/api/pith-number/CIAKGNSOTQSIWUY2VKBO7A7FWK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CIAKGNSOTQSIWUY2VKBO7A7FWK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CIAKGNSOTQSIWUY2VKBO7A7FWK/action/storage_attestation","attest_author":"https://pith.science/pith/CIAKGNSOTQSIWUY2VKBO7A7FWK/action/author_attestation","sign_citation":"https://pith.science/pith/CIAKGNSOTQSIWUY2VKBO7A7FWK/action/citation_signature","submit_replication":"https://pith.science/pith/CIAKGNSOTQSIWUY2VKBO7A7FWK/action/replication_record"}},"created_at":"2026-07-05T00:15:02.911250+00:00","updated_at":"2026-07-05T00:15:02.911250+00:00"}