{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:ZH6WDANIJUEPC5ETO7LVUQHHQW","short_pith_number":"pith:ZH6WDANI","canonical_record":{"source":{"id":"2208.09688","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-08-20T14:15:35Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"42d148221c04b99dbd0987c74fca4ef98fe66bbc5fca2a3318204dd2d074f9da","abstract_canon_sha256":"8b45de00d3697662de37f0cc8cea192d7fae7ce5b041c4b8e5492603d995b172"},"schema_version":"1.0"},"canonical_sha256":"c9fd6181a84d08f1749377d75a40e785ba1687d8f9f86413a97acf75ceadeeb0","source":{"kind":"arxiv","id":"2208.09688","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2208.09688","created_at":"2026-07-05T07:14:50Z"},{"alias_kind":"arxiv_version","alias_value":"2208.09688v3","created_at":"2026-07-05T07:14:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.09688","created_at":"2026-07-05T07:14:50Z"},{"alias_kind":"pith_short_12","alias_value":"ZH6WDANIJUEP","created_at":"2026-07-05T07:14:50Z"},{"alias_kind":"pith_short_16","alias_value":"ZH6WDANIJUEPC5ET","created_at":"2026-07-05T07:14:50Z"},{"alias_kind":"pith_short_8","alias_value":"ZH6WDANI","created_at":"2026-07-05T07:14:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:ZH6WDANIJUEPC5ETO7LVUQHHQW","target":"record","payload":{"canonical_record":{"source":{"id":"2208.09688","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-08-20T14:15:35Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"42d148221c04b99dbd0987c74fca4ef98fe66bbc5fca2a3318204dd2d074f9da","abstract_canon_sha256":"8b45de00d3697662de37f0cc8cea192d7fae7ce5b041c4b8e5492603d995b172"},"schema_version":"1.0"},"canonical_sha256":"c9fd6181a84d08f1749377d75a40e785ba1687d8f9f86413a97acf75ceadeeb0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:14:50.796959Z","signature_b64":"eU7OGwzWhDiS4zmiSAiG5yuGjJfAVUybsXc8uO2aHstxF5D73Yv8ht6C77qldlAxAn4lYcG2IBmxFqXe1GXeBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c9fd6181a84d08f1749377d75a40e785ba1687d8f9f86413a97acf75ceadeeb0","last_reissued_at":"2026-07-05T07:14:50.796509Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:14:50.796509Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2208.09688","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:14:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Gkps8SfuaufSdwoVeO+5d0ZtO0ptwbfRoz9gKZEUy1i0+XWlhcLCqIuBBagLTZqDz48dzGxFtNTcJDQFXWJgDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T12:44:44.078129Z"},"content_sha256":"3df10f1cb6e53cd334b4ef0ab67aa3cf4c8f75599482a273ed63ac3e97454a17","schema_version":"1.0","event_id":"sha256:3df10f1cb6e53cd334b4ef0ab67aa3cf4c8f75599482a273ed63ac3e97454a17"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:ZH6WDANIJUEPC5ETO7LVUQHHQW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Sub-Pixel Disparity Distribution for Light Field Depth Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Fuqing Duan, Guanghui Wang, Wentao Chao, Xuechun Wang, Yingqian Wang","submitted_at":"2022-08-20T14:15:35Z","abstract_excerpt":"Light field (LF) depth estimation plays a crucial role in many LF-based applications. Existing LF depth estimation methods consider depth estimation as a regression problem, where a pixel-wise L1 loss is employed to supervise the training process. However, the disparity map is only a sub-space projection (i.e., an expectation) of the disparity distribution, which is essential for models to learn. In this paper, we propose a simple yet effective method to learn the sub-pixel disparity distribution by fully utilizing the power of deep networks, especially for LF of narrow baselines. We construct"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.09688","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/2208.09688/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:14:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7METrfOrw7ZJooE61BmZahO1ZzlHAM0eQqDgI0wF6HvRrU8cw1iU63M10N3pnPGK3SrImEtiIEaAk5tAWRlxCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T12:44:44.078889Z"},"content_sha256":"f562093b8a96e6ba8acc318acb2dd763e90eaf69e16ca41476da1dd6694e3522","schema_version":"1.0","event_id":"sha256:f562093b8a96e6ba8acc318acb2dd763e90eaf69e16ca41476da1dd6694e3522"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZH6WDANIJUEPC5ETO7LVUQHHQW/bundle.json","state_url":"https://pith.science/pith/ZH6WDANIJUEPC5ETO7LVUQHHQW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZH6WDANIJUEPC5ETO7LVUQHHQW/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-23T12:44:44Z","links":{"resolver":"https://pith.science/pith/ZH6WDANIJUEPC5ETO7LVUQHHQW","bundle":"https://pith.science/pith/ZH6WDANIJUEPC5ETO7LVUQHHQW/bundle.json","state":"https://pith.science/pith/ZH6WDANIJUEPC5ETO7LVUQHHQW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZH6WDANIJUEPC5ETO7LVUQHHQW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:ZH6WDANIJUEPC5ETO7LVUQHHQW","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"8b45de00d3697662de37f0cc8cea192d7fae7ce5b041c4b8e5492603d995b172","cross_cats_sorted":["eess.IV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-08-20T14:15:35Z","title_canon_sha256":"42d148221c04b99dbd0987c74fca4ef98fe66bbc5fca2a3318204dd2d074f9da"},"schema_version":"1.0","source":{"id":"2208.09688","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2208.09688","created_at":"2026-07-05T07:14:50Z"},{"alias_kind":"arxiv_version","alias_value":"2208.09688v3","created_at":"2026-07-05T07:14:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.09688","created_at":"2026-07-05T07:14:50Z"},{"alias_kind":"pith_short_12","alias_value":"ZH6WDANIJUEP","created_at":"2026-07-05T07:14:50Z"},{"alias_kind":"pith_short_16","alias_value":"ZH6WDANIJUEPC5ET","created_at":"2026-07-05T07:14:50Z"},{"alias_kind":"pith_short_8","alias_value":"ZH6WDANI","created_at":"2026-07-05T07:14:50Z"}],"graph_snapshots":[{"event_id":"sha256:f562093b8a96e6ba8acc318acb2dd763e90eaf69e16ca41476da1dd6694e3522","target":"graph","created_at":"2026-07-05T07:14:50Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2208.09688/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Light field (LF) depth estimation plays a crucial role in many LF-based applications. Existing LF depth estimation methods consider depth estimation as a regression problem, where a pixel-wise L1 loss is employed to supervise the training process. However, the disparity map is only a sub-space projection (i.e., an expectation) of the disparity distribution, which is essential for models to learn. In this paper, we propose a simple yet effective method to learn the sub-pixel disparity distribution by fully utilizing the power of deep networks, especially for LF of narrow baselines. We construct","authors_text":"Fuqing Duan, Guanghui Wang, Wentao Chao, Xuechun Wang, Yingqian Wang","cross_cats":["eess.IV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-08-20T14:15:35Z","title":"Learning Sub-Pixel Disparity Distribution for Light Field Depth Estimation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.09688","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:3df10f1cb6e53cd334b4ef0ab67aa3cf4c8f75599482a273ed63ac3e97454a17","target":"record","created_at":"2026-07-05T07:14:50Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"8b45de00d3697662de37f0cc8cea192d7fae7ce5b041c4b8e5492603d995b172","cross_cats_sorted":["eess.IV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-08-20T14:15:35Z","title_canon_sha256":"42d148221c04b99dbd0987c74fca4ef98fe66bbc5fca2a3318204dd2d074f9da"},"schema_version":"1.0","source":{"id":"2208.09688","kind":"arxiv","version":3}},"canonical_sha256":"c9fd6181a84d08f1749377d75a40e785ba1687d8f9f86413a97acf75ceadeeb0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c9fd6181a84d08f1749377d75a40e785ba1687d8f9f86413a97acf75ceadeeb0","first_computed_at":"2026-07-05T07:14:50.796509Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:14:50.796509Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"eU7OGwzWhDiS4zmiSAiG5yuGjJfAVUybsXc8uO2aHstxF5D73Yv8ht6C77qldlAxAn4lYcG2IBmxFqXe1GXeBw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:14:50.796959Z","signed_message":"canonical_sha256_bytes"},"source_id":"2208.09688","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3df10f1cb6e53cd334b4ef0ab67aa3cf4c8f75599482a273ed63ac3e97454a17","sha256:f562093b8a96e6ba8acc318acb2dd763e90eaf69e16ca41476da1dd6694e3522"],"state_sha256":"ba7a4d27eff32ac6ae889da3f82f3658be8d8ff3c9694bf9981138e8ab8c5bf2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dRfPYsoFPLi3ehKyXhFTJ68vi5/ODkpz6SoXKnpmQbDp3EfE6NlxN3X6xZhl4xxCInnWSTjNGHVRr+7HuECRAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-23T12:44:44.084937Z","bundle_sha256":"fcf8c5499b9bf64ef618beba6184d19bc7599459f1b3a97c5892e682d73111b7"}}