{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:UAMBX5XJNV4HPS2G6SCVWLVAYA","short_pith_number":"pith:UAMBX5XJ","schema_version":"1.0","canonical_sha256":"a0181bf6e96d7877cb46f4855b2ea0c009672a760fd0ea14a88ce70b697bbecc","source":{"kind":"arxiv","id":"2103.00760","version":3},"attestation_state":"computed","paper":{"title":"Self-Supervised Depth and Ego-Motion Estimation for Monocular Thermal Video Using Multi-Spectral Consistency Loss","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"In So Kweon, Kyunghyun Lee, Seokju Lee, Ukcheol Shin","submitted_at":"2021-03-01T05:29:04Z","abstract_excerpt":"A thermal camera can robustly capture thermal radiation images under harsh light conditions such as night scenes, tunnels, and disaster scenarios. However, despite this advantage, neither depth nor ego-motion estimation research for the thermal camera have not been actively explored so far. In this paper, we propose a self-supervised learning method for depth and ego-motion estimation from thermal images. The proposed method exploits multi-spectral consistency that consists of temperature and photometric consistency loss. The temperature consistency loss provides a fundamental self-supervisory"},"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":"2103.00760","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2021-03-01T05:29:04Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"cf1660ef146d3d0919f3cefcdbcaa264f9b78ce0bf15597f7544bcf8770d9f93","abstract_canon_sha256":"8af76cd763c52754a6c1cd60b1a656351e2b437ab0edd9537a66a142710c220b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:38:10.282962Z","signature_b64":"YU9MXnAPzYCw2MH8G48V7J5hMNcD31RL+z6yOxc8T8U9lSdFFxUqofawvboffj7ddU765ejDDs2OGKfgGtEGBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a0181bf6e96d7877cb46f4855b2ea0c009672a760fd0ea14a88ce70b697bbecc","last_reissued_at":"2026-07-05T04:38:10.282494Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:38:10.282494Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Self-Supervised Depth and Ego-Motion Estimation for Monocular Thermal Video Using Multi-Spectral Consistency Loss","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"In So Kweon, Kyunghyun Lee, Seokju Lee, Ukcheol Shin","submitted_at":"2021-03-01T05:29:04Z","abstract_excerpt":"A thermal camera can robustly capture thermal radiation images under harsh light conditions such as night scenes, tunnels, and disaster scenarios. However, despite this advantage, neither depth nor ego-motion estimation research for the thermal camera have not been actively explored so far. In this paper, we propose a self-supervised learning method for depth and ego-motion estimation from thermal images. The proposed method exploits multi-spectral consistency that consists of temperature and photometric consistency loss. The temperature consistency loss provides a fundamental self-supervisory"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.00760","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/2103.00760/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":"2103.00760","created_at":"2026-07-05T04:38:10.282554+00:00"},{"alias_kind":"arxiv_version","alias_value":"2103.00760v3","created_at":"2026-07-05T04:38:10.282554+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.00760","created_at":"2026-07-05T04:38:10.282554+00:00"},{"alias_kind":"pith_short_12","alias_value":"UAMBX5XJNV4H","created_at":"2026-07-05T04:38:10.282554+00:00"},{"alias_kind":"pith_short_16","alias_value":"UAMBX5XJNV4HPS2G","created_at":"2026-07-05T04:38:10.282554+00:00"},{"alias_kind":"pith_short_8","alias_value":"UAMBX5XJ","created_at":"2026-07-05T04:38:10.282554+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/UAMBX5XJNV4HPS2G6SCVWLVAYA","json":"https://pith.science/pith/UAMBX5XJNV4HPS2G6SCVWLVAYA.json","graph_json":"https://pith.science/api/pith-number/UAMBX5XJNV4HPS2G6SCVWLVAYA/graph.json","events_json":"https://pith.science/api/pith-number/UAMBX5XJNV4HPS2G6SCVWLVAYA/events.json","paper":"https://pith.science/paper/UAMBX5XJ"},"agent_actions":{"view_html":"https://pith.science/pith/UAMBX5XJNV4HPS2G6SCVWLVAYA","download_json":"https://pith.science/pith/UAMBX5XJNV4HPS2G6SCVWLVAYA.json","view_paper":"https://pith.science/paper/UAMBX5XJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2103.00760&json=true","fetch_graph":"https://pith.science/api/pith-number/UAMBX5XJNV4HPS2G6SCVWLVAYA/graph.json","fetch_events":"https://pith.science/api/pith-number/UAMBX5XJNV4HPS2G6SCVWLVAYA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UAMBX5XJNV4HPS2G6SCVWLVAYA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UAMBX5XJNV4HPS2G6SCVWLVAYA/action/storage_attestation","attest_author":"https://pith.science/pith/UAMBX5XJNV4HPS2G6SCVWLVAYA/action/author_attestation","sign_citation":"https://pith.science/pith/UAMBX5XJNV4HPS2G6SCVWLVAYA/action/citation_signature","submit_replication":"https://pith.science/pith/UAMBX5XJNV4HPS2G6SCVWLVAYA/action/replication_record"}},"created_at":"2026-07-05T04:38:10.282554+00:00","updated_at":"2026-07-05T04:38:10.282554+00:00"}