{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:RDMKQ76PWD6W4HCTULIS4KVX64","short_pith_number":"pith:RDMKQ76P","schema_version":"1.0","canonical_sha256":"88d8a87fcfb0fd6e1c53a2d12e2ab7f737b621718f98698b2bc26ac9470e9aee","source":{"kind":"arxiv","id":"1811.07124","version":1},"attestation_state":"computed","paper":{"title":"VommaNet: an End-to-End Network for Disparity Estimation from Reflective and Texture-less Light Field Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Haotian Li, Haoxin Ma, Shengxian Shi, Tingting Mu, Zhiwen Qian","submitted_at":"2018-11-17T08:13:17Z","abstract_excerpt":"The precise combination of image sensor and micro-lens array enables lenslet light field cameras to record both angular and spatial information of incoming light, therefore, one can calculate disparity and depth from light field images. In turn, 3D models of the recorded objects can be recovered, which is a great advantage over other imaging system. However, reflective and texture-less areas in light field images have complicated conditions, making it hard to correctly calculate disparity with existing algorithms. To tackle this problem, we introduce a novel end-to-end network VommaNet to retr"},"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":"1811.07124","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-17T08:13:17Z","cross_cats_sorted":[],"title_canon_sha256":"807e7be6a7fb4798294b9f720434137288394e5ab0527e1c802f240b45b24aac","abstract_canon_sha256":"07df4b2b92d0ed7dcaba0b34862e07ef47add7dd7b311eb7b5e5c1e4e99fbf5c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:00:28.885012Z","signature_b64":"FZOezGD+PS0Nl+ShQd7Y/LhKJh4e4xFBDMAn5z6o3tK6FcUtiMakvCxmvFFcMgbr3QBxJOp52naDVY3dPEw1Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"88d8a87fcfb0fd6e1c53a2d12e2ab7f737b621718f98698b2bc26ac9470e9aee","last_reissued_at":"2026-05-18T00:00:28.884311Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:00:28.884311Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"VommaNet: an End-to-End Network for Disparity Estimation from Reflective and Texture-less Light Field Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Haotian Li, Haoxin Ma, Shengxian Shi, Tingting Mu, Zhiwen Qian","submitted_at":"2018-11-17T08:13:17Z","abstract_excerpt":"The precise combination of image sensor and micro-lens array enables lenslet light field cameras to record both angular and spatial information of incoming light, therefore, one can calculate disparity and depth from light field images. In turn, 3D models of the recorded objects can be recovered, which is a great advantage over other imaging system. However, reflective and texture-less areas in light field images have complicated conditions, making it hard to correctly calculate disparity with existing algorithms. To tackle this problem, we introduce a novel end-to-end network VommaNet to retr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.07124","kind":"arxiv","version":1},"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":"1811.07124","created_at":"2026-05-18T00:00:28.884435+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.07124v1","created_at":"2026-05-18T00:00:28.884435+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.07124","created_at":"2026-05-18T00:00:28.884435+00:00"},{"alias_kind":"pith_short_12","alias_value":"RDMKQ76PWD6W","created_at":"2026-05-18T12:32:50.500415+00:00"},{"alias_kind":"pith_short_16","alias_value":"RDMKQ76PWD6W4HCT","created_at":"2026-05-18T12:32:50.500415+00:00"},{"alias_kind":"pith_short_8","alias_value":"RDMKQ76P","created_at":"2026-05-18T12:32:50.500415+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/RDMKQ76PWD6W4HCTULIS4KVX64","json":"https://pith.science/pith/RDMKQ76PWD6W4HCTULIS4KVX64.json","graph_json":"https://pith.science/api/pith-number/RDMKQ76PWD6W4HCTULIS4KVX64/graph.json","events_json":"https://pith.science/api/pith-number/RDMKQ76PWD6W4HCTULIS4KVX64/events.json","paper":"https://pith.science/paper/RDMKQ76P"},"agent_actions":{"view_html":"https://pith.science/pith/RDMKQ76PWD6W4HCTULIS4KVX64","download_json":"https://pith.science/pith/RDMKQ76PWD6W4HCTULIS4KVX64.json","view_paper":"https://pith.science/paper/RDMKQ76P","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.07124&json=true","fetch_graph":"https://pith.science/api/pith-number/RDMKQ76PWD6W4HCTULIS4KVX64/graph.json","fetch_events":"https://pith.science/api/pith-number/RDMKQ76PWD6W4HCTULIS4KVX64/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RDMKQ76PWD6W4HCTULIS4KVX64/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RDMKQ76PWD6W4HCTULIS4KVX64/action/storage_attestation","attest_author":"https://pith.science/pith/RDMKQ76PWD6W4HCTULIS4KVX64/action/author_attestation","sign_citation":"https://pith.science/pith/RDMKQ76PWD6W4HCTULIS4KVX64/action/citation_signature","submit_replication":"https://pith.science/pith/RDMKQ76PWD6W4HCTULIS4KVX64/action/replication_record"}},"created_at":"2026-05-18T00:00:28.884435+00:00","updated_at":"2026-05-18T00:00:28.884435+00:00"}