{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:JF3N6ORDGARKCMQZ3LGZUQCFDT","short_pith_number":"pith:JF3N6ORD","schema_version":"1.0","canonical_sha256":"4976df3a233022a13219dacd9a40451cfd6444da6b9bd7017d01d798c1925965","source":{"kind":"arxiv","id":"1812.00488","version":2},"attestation_state":"computed","paper":{"title":"DeepLiDAR: Deep Surface Normal Guided Depth Prediction for Outdoor Scene from Sparse LiDAR Data and Single Color Image","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bing Zeng, Jiaxiong Qiu, Marc Pollefeys, Shuaicheng Liu, Xingdi Zhang, Yinda Zhang, Zhaopeng Cui","submitted_at":"2018-12-02T23:36:22Z","abstract_excerpt":"In this paper, we propose a deep learning architecture that produces accurate dense depth for the outdoor scene from a single color image and a sparse depth. Inspired by the indoor depth completion, our network estimates surface normals as the intermediate representation to produce dense depth, and can be trained end-to-end. With a modified encoder-decoder structure, our network effectively fuses the dense color image and the sparse LiDAR depth. To address outdoor specific challenges, our network predicts a confidence mask to handle mixed LiDAR signals near foreground boundaries due to occlusi"},"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":"1812.00488","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-02T23:36:22Z","cross_cats_sorted":[],"title_canon_sha256":"f85fdde980e7912af7fb237bc6e0615de4cc6b1791c0f191efdb4a7341764af1","abstract_canon_sha256":"bdd19c02e263547aebc73ecea83b5770ebac9777ca8e69c29b263012faa71f56"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:48:57.980071Z","signature_b64":"yfy5Rd81Xa5aoSKEDiP7xlRqesAg0F8opuufBdYcUCtVl0b9FgIDgzcFSPmDoVUVakHs9+DaMz4hQH2PBNPmAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4976df3a233022a13219dacd9a40451cfd6444da6b9bd7017d01d798c1925965","last_reissued_at":"2026-05-17T23:48:57.979381Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:48:57.979381Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DeepLiDAR: Deep Surface Normal Guided Depth Prediction for Outdoor Scene from Sparse LiDAR Data and Single Color Image","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bing Zeng, Jiaxiong Qiu, Marc Pollefeys, Shuaicheng Liu, Xingdi Zhang, Yinda Zhang, Zhaopeng Cui","submitted_at":"2018-12-02T23:36:22Z","abstract_excerpt":"In this paper, we propose a deep learning architecture that produces accurate dense depth for the outdoor scene from a single color image and a sparse depth. Inspired by the indoor depth completion, our network estimates surface normals as the intermediate representation to produce dense depth, and can be trained end-to-end. With a modified encoder-decoder structure, our network effectively fuses the dense color image and the sparse LiDAR depth. To address outdoor specific challenges, our network predicts a confidence mask to handle mixed LiDAR signals near foreground boundaries due to occlusi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.00488","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":""},"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":"1812.00488","created_at":"2026-05-17T23:48:57.979491+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.00488v2","created_at":"2026-05-17T23:48:57.979491+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.00488","created_at":"2026-05-17T23:48:57.979491+00:00"},{"alias_kind":"pith_short_12","alias_value":"JF3N6ORDGARK","created_at":"2026-05-18T12:32:31.084164+00:00"},{"alias_kind":"pith_short_16","alias_value":"JF3N6ORDGARKCMQZ","created_at":"2026-05-18T12:32:31.084164+00:00"},{"alias_kind":"pith_short_8","alias_value":"JF3N6ORD","created_at":"2026-05-18T12:32:31.084164+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/JF3N6ORDGARKCMQZ3LGZUQCFDT","json":"https://pith.science/pith/JF3N6ORDGARKCMQZ3LGZUQCFDT.json","graph_json":"https://pith.science/api/pith-number/JF3N6ORDGARKCMQZ3LGZUQCFDT/graph.json","events_json":"https://pith.science/api/pith-number/JF3N6ORDGARKCMQZ3LGZUQCFDT/events.json","paper":"https://pith.science/paper/JF3N6ORD"},"agent_actions":{"view_html":"https://pith.science/pith/JF3N6ORDGARKCMQZ3LGZUQCFDT","download_json":"https://pith.science/pith/JF3N6ORDGARKCMQZ3LGZUQCFDT.json","view_paper":"https://pith.science/paper/JF3N6ORD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.00488&json=true","fetch_graph":"https://pith.science/api/pith-number/JF3N6ORDGARKCMQZ3LGZUQCFDT/graph.json","fetch_events":"https://pith.science/api/pith-number/JF3N6ORDGARKCMQZ3LGZUQCFDT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JF3N6ORDGARKCMQZ3LGZUQCFDT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JF3N6ORDGARKCMQZ3LGZUQCFDT/action/storage_attestation","attest_author":"https://pith.science/pith/JF3N6ORDGARKCMQZ3LGZUQCFDT/action/author_attestation","sign_citation":"https://pith.science/pith/JF3N6ORDGARKCMQZ3LGZUQCFDT/action/citation_signature","submit_replication":"https://pith.science/pith/JF3N6ORDGARKCMQZ3LGZUQCFDT/action/replication_record"}},"created_at":"2026-05-17T23:48:57.979491+00:00","updated_at":"2026-05-17T23:48:57.979491+00:00"}