{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:NBJDZZNGGA7T7NWRJ2NO7ARU5B","short_pith_number":"pith:NBJDZZNG","schema_version":"1.0","canonical_sha256":"68523ce5a6303f3fb6d14e9aef8234e86de9e5ac387e95f61b09931ae154b37e","source":{"kind":"arxiv","id":"1902.10904","version":2},"attestation_state":"computed","paper":{"title":"SweepNet: Wide-baseline Omnidirectional Depth Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Changhee Won, Jongbin Ryu, Jongwoo Lim","submitted_at":"2019-02-28T05:36:19Z","abstract_excerpt":"Omnidirectional depth sensing has its advantage over the conventional stereo systems since it enables us to recognize the objects of interest in all directions without any blind regions. In this paper, we propose a novel wide-baseline omnidirectional stereo algorithm which computes the dense depth estimate from the fisheye images using a deep convolutional neural network. The capture system consists of multiple cameras mounted on a wide-baseline rig with ultrawide field of view (FOV) lenses, and we present the calibration algorithm for the extrinsic parameters based on the bundle adjustment. I"},"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":"1902.10904","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-02-28T05:36:19Z","cross_cats_sorted":[],"title_canon_sha256":"569b2d62bd1ffd2ebbbcf75a2a367fe41a700f641db857bf10b8cbb1e72b7964","abstract_canon_sha256":"eaa9bdbf783e694049e31d4d4dbcde0dc1e2fa2b98ee38c97694a3cdd649f15e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-04T23:58:00.475283Z","signature_b64":"02UB8YAcGvyu5/VNklJ7fcNXSGpewZGA0x7VxijhrKqJJ4ZG9+Od/ua4thqY/PPnEdbQVzVxBM7uf5Ekxc31BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"68523ce5a6303f3fb6d14e9aef8234e86de9e5ac387e95f61b09931ae154b37e","last_reissued_at":"2026-07-04T23:58:00.474933Z","signature_status":"signed_v1","first_computed_at":"2026-07-04T23:58:00.474933Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SweepNet: Wide-baseline Omnidirectional Depth Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Changhee Won, Jongbin Ryu, Jongwoo Lim","submitted_at":"2019-02-28T05:36:19Z","abstract_excerpt":"Omnidirectional depth sensing has its advantage over the conventional stereo systems since it enables us to recognize the objects of interest in all directions without any blind regions. In this paper, we propose a novel wide-baseline omnidirectional stereo algorithm which computes the dense depth estimate from the fisheye images using a deep convolutional neural network. The capture system consists of multiple cameras mounted on a wide-baseline rig with ultrawide field of view (FOV) lenses, and we present the calibration algorithm for the extrinsic parameters based on the bundle adjustment. I"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.10904","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1902.10904/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":"1902.10904","created_at":"2026-07-04T23:58:00.474986+00:00"},{"alias_kind":"arxiv_version","alias_value":"1902.10904v2","created_at":"2026-07-04T23:58:00.474986+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.10904","created_at":"2026-07-04T23:58:00.474986+00:00"},{"alias_kind":"pith_short_12","alias_value":"NBJDZZNGGA7T","created_at":"2026-07-04T23:58:00.474986+00:00"},{"alias_kind":"pith_short_16","alias_value":"NBJDZZNGGA7T7NWR","created_at":"2026-07-04T23:58:00.474986+00:00"},{"alias_kind":"pith_short_8","alias_value":"NBJDZZNG","created_at":"2026-07-04T23:58:00.474986+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1908.06257","citing_title":"OmniMVS: End-to-End Learning for Omnidirectional Stereo Matching","ref_index":32,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NBJDZZNGGA7T7NWRJ2NO7ARU5B","json":"https://pith.science/pith/NBJDZZNGGA7T7NWRJ2NO7ARU5B.json","graph_json":"https://pith.science/api/pith-number/NBJDZZNGGA7T7NWRJ2NO7ARU5B/graph.json","events_json":"https://pith.science/api/pith-number/NBJDZZNGGA7T7NWRJ2NO7ARU5B/events.json","paper":"https://pith.science/paper/NBJDZZNG"},"agent_actions":{"view_html":"https://pith.science/pith/NBJDZZNGGA7T7NWRJ2NO7ARU5B","download_json":"https://pith.science/pith/NBJDZZNGGA7T7NWRJ2NO7ARU5B.json","view_paper":"https://pith.science/paper/NBJDZZNG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1902.10904&json=true","fetch_graph":"https://pith.science/api/pith-number/NBJDZZNGGA7T7NWRJ2NO7ARU5B/graph.json","fetch_events":"https://pith.science/api/pith-number/NBJDZZNGGA7T7NWRJ2NO7ARU5B/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NBJDZZNGGA7T7NWRJ2NO7ARU5B/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NBJDZZNGGA7T7NWRJ2NO7ARU5B/action/storage_attestation","attest_author":"https://pith.science/pith/NBJDZZNGGA7T7NWRJ2NO7ARU5B/action/author_attestation","sign_citation":"https://pith.science/pith/NBJDZZNGGA7T7NWRJ2NO7ARU5B/action/citation_signature","submit_replication":"https://pith.science/pith/NBJDZZNGGA7T7NWRJ2NO7ARU5B/action/replication_record"}},"created_at":"2026-07-04T23:58:00.474986+00:00","updated_at":"2026-07-04T23:58:00.474986+00:00"}