{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:LZUGFFE4APANW3PMUXX6VK6RLF","short_pith_number":"pith:LZUGFFE4","schema_version":"1.0","canonical_sha256":"5e6862949c03c0db6deca5efeaabd1596d488acba40c11fe3a775bba931f5d7e","source":{"kind":"arxiv","id":"1807.00275","version":2},"attestation_state":"computed","paper":{"title":"Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Camera","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.RO"],"primary_cat":"cs.CV","authors_text":"Fangchang Ma, Guilherme Venturelli Cavalheiro, Sertac Karaman","submitted_at":"2018-07-01T06:02:48Z","abstract_excerpt":"Depth completion, the technique of estimating a dense depth image from sparse depth measurements, has a variety of applications in robotics and autonomous driving. However, depth completion faces 3 main challenges: the irregularly spaced pattern in the sparse depth input, the difficulty in handling multiple sensor modalities (when color images are available), as well as the lack of dense, pixel-level ground truth depth labels. In this work, we address all these challenges. Specifically, we develop a deep regression model to learn a direct mapping from sparse depth (and color images) to dense d"},"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":"1807.00275","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-01T06:02:48Z","cross_cats_sorted":["cs.AI","cs.LG","cs.RO"],"title_canon_sha256":"eecf4966121eff2c70be339dea9dcd3cbc9249fb7b534389fa7e5224fc74d18f","abstract_canon_sha256":"ac2f44b8a782f8d1874796f92b3769b693c1a646023cba18cbab4488c96a08ee"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:11:46.413555Z","signature_b64":"wWoiM94HhKiux+p4Ljomqojyj01ilstys4PBDcVoKMEWM1cv8ynx2QkqsFbLz3MDtw1feKqXl8bO6z2Z1q09CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5e6862949c03c0db6deca5efeaabd1596d488acba40c11fe3a775bba931f5d7e","last_reissued_at":"2026-05-18T00:11:46.411938Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:11:46.411938Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Camera","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.RO"],"primary_cat":"cs.CV","authors_text":"Fangchang Ma, Guilherme Venturelli Cavalheiro, Sertac Karaman","submitted_at":"2018-07-01T06:02:48Z","abstract_excerpt":"Depth completion, the technique of estimating a dense depth image from sparse depth measurements, has a variety of applications in robotics and autonomous driving. However, depth completion faces 3 main challenges: the irregularly spaced pattern in the sparse depth input, the difficulty in handling multiple sensor modalities (when color images are available), as well as the lack of dense, pixel-level ground truth depth labels. In this work, we address all these challenges. Specifically, we develop a deep regression model to learn a direct mapping from sparse depth (and color images) to dense d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.00275","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":"1807.00275","created_at":"2026-05-18T00:11:46.412040+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.00275v2","created_at":"2026-05-18T00:11:46.412040+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.00275","created_at":"2026-05-18T00:11:46.412040+00:00"},{"alias_kind":"pith_short_12","alias_value":"LZUGFFE4APAN","created_at":"2026-05-18T12:32:37.024351+00:00"},{"alias_kind":"pith_short_16","alias_value":"LZUGFFE4APANW3PM","created_at":"2026-05-18T12:32:37.024351+00:00"},{"alias_kind":"pith_short_8","alias_value":"LZUGFFE4","created_at":"2026-05-18T12:32:37.024351+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/LZUGFFE4APANW3PMUXX6VK6RLF","json":"https://pith.science/pith/LZUGFFE4APANW3PMUXX6VK6RLF.json","graph_json":"https://pith.science/api/pith-number/LZUGFFE4APANW3PMUXX6VK6RLF/graph.json","events_json":"https://pith.science/api/pith-number/LZUGFFE4APANW3PMUXX6VK6RLF/events.json","paper":"https://pith.science/paper/LZUGFFE4"},"agent_actions":{"view_html":"https://pith.science/pith/LZUGFFE4APANW3PMUXX6VK6RLF","download_json":"https://pith.science/pith/LZUGFFE4APANW3PMUXX6VK6RLF.json","view_paper":"https://pith.science/paper/LZUGFFE4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.00275&json=true","fetch_graph":"https://pith.science/api/pith-number/LZUGFFE4APANW3PMUXX6VK6RLF/graph.json","fetch_events":"https://pith.science/api/pith-number/LZUGFFE4APANW3PMUXX6VK6RLF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LZUGFFE4APANW3PMUXX6VK6RLF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LZUGFFE4APANW3PMUXX6VK6RLF/action/storage_attestation","attest_author":"https://pith.science/pith/LZUGFFE4APANW3PMUXX6VK6RLF/action/author_attestation","sign_citation":"https://pith.science/pith/LZUGFFE4APANW3PMUXX6VK6RLF/action/citation_signature","submit_replication":"https://pith.science/pith/LZUGFFE4APANW3PMUXX6VK6RLF/action/replication_record"}},"created_at":"2026-05-18T00:11:46.412040+00:00","updated_at":"2026-05-18T00:11:46.412040+00:00"}