{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:SWTX4E65HZAKCUQX2PFNNO657I","short_pith_number":"pith:SWTX4E65","canonical_record":{"source":{"id":"1607.00730","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-07-04T03:22:45Z","cross_cats_sorted":[],"title_canon_sha256":"fe0e768cf48878eecb7043f8370d75879d15d82ab91f92a654353b6e4152aed5","abstract_canon_sha256":"42f38acbe0f4f2dc70bc82fc09b2d265799e087c4c91a6599f529d1d38d26c49"},"schema_version":"1.0"},"canonical_sha256":"95a77e13dd3e40a15217d3cad6bbddfa047ddbde3a8d6677ccae5dc45d5c062d","source":{"kind":"arxiv","id":"1607.00730","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1607.00730","created_at":"2026-05-18T00:29:01Z"},{"alias_kind":"arxiv_version","alias_value":"1607.00730v4","created_at":"2026-05-18T00:29:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1607.00730","created_at":"2026-05-18T00:29:01Z"},{"alias_kind":"pith_short_12","alias_value":"SWTX4E65HZAK","created_at":"2026-05-18T12:30:44Z"},{"alias_kind":"pith_short_16","alias_value":"SWTX4E65HZAKCUQX","created_at":"2026-05-18T12:30:44Z"},{"alias_kind":"pith_short_8","alias_value":"SWTX4E65","created_at":"2026-05-18T12:30:44Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:SWTX4E65HZAKCUQX2PFNNO657I","target":"record","payload":{"canonical_record":{"source":{"id":"1607.00730","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-07-04T03:22:45Z","cross_cats_sorted":[],"title_canon_sha256":"fe0e768cf48878eecb7043f8370d75879d15d82ab91f92a654353b6e4152aed5","abstract_canon_sha256":"42f38acbe0f4f2dc70bc82fc09b2d265799e087c4c91a6599f529d1d38d26c49"},"schema_version":"1.0"},"canonical_sha256":"95a77e13dd3e40a15217d3cad6bbddfa047ddbde3a8d6677ccae5dc45d5c062d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:29:01.928033Z","signature_b64":"wtg+/pykS7D9F3QCuSp6GoR39yT9hcDccMFzQMOZoHK6jzP1AyndyhQjjl6wNSLh+ZI4kgF0tRiUjqP2h/l2BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"95a77e13dd3e40a15217d3cad6bbddfa047ddbde3a8d6677ccae5dc45d5c062d","last_reissued_at":"2026-05-18T00:29:01.926724Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:29:01.926724Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1607.00730","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:29:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0o0J49KBFA5uj2e8vgIZdjnM9q/iNtZpbXO1VKjUC45mlacMCJPGgEewHbN7m/U4Yk1oL/zMxdKPNfIIWgPxBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-20T15:16:12.681873Z"},"content_sha256":"b4af1ce4f145f0c74c2bff0db52ab1b89a69307b5fcd59ab4605a99857d860ca","schema_version":"1.0","event_id":"sha256:b4af1ce4f145f0c74c2bff0db52ab1b89a69307b5fcd59ab4605a99857d860ca"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:SWTX4E65HZAKCUQX2PFNNO657I","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Two-Streamed Network for Estimating Fine-Scaled Depth Maps from Single RGB Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Angela Yao, Jun Li, Reinhard Klein","submitted_at":"2016-07-04T03:22:45Z","abstract_excerpt":"Estimating depth from a single RGB image is an ill-posed and inherently ambiguous problem. State-of-the-art deep learning methods can now estimate accurate 2D depth maps, but when the maps are projected into 3D, they lack local detail and are often highly distorted. We propose a fast-to-train two-streamed CNN that predicts depth and depth gradients, which are then fused together into an accurate and detailed depth map. We also define a novel set loss over multiple images; by regularizing the estimation between a common set of images, the network is less prone to over-fitting and achieves bette"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1607.00730","kind":"arxiv","version":4},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:29:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QlXHR19iR0+dIgtuBhvrVFjVsJZort2D663jEpt+6Mvm7eQ5/vin9sFt1qOr7LkO/7txNqXVgfPQvoz53NimAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-20T15:16:12.682222Z"},"content_sha256":"8259a2a048a1230855c3a3a62c060719ca4d3ef8c099d2cdd4d537bac0b6e6e5","schema_version":"1.0","event_id":"sha256:8259a2a048a1230855c3a3a62c060719ca4d3ef8c099d2cdd4d537bac0b6e6e5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SWTX4E65HZAKCUQX2PFNNO657I/bundle.json","state_url":"https://pith.science/pith/SWTX4E65HZAKCUQX2PFNNO657I/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SWTX4E65HZAKCUQX2PFNNO657I/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":"2026-05-20T15:16:12Z","links":{"resolver":"https://pith.science/pith/SWTX4E65HZAKCUQX2PFNNO657I","bundle":"https://pith.science/pith/SWTX4E65HZAKCUQX2PFNNO657I/bundle.json","state":"https://pith.science/pith/SWTX4E65HZAKCUQX2PFNNO657I/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SWTX4E65HZAKCUQX2PFNNO657I/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:SWTX4E65HZAKCUQX2PFNNO657I","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":"42f38acbe0f4f2dc70bc82fc09b2d265799e087c4c91a6599f529d1d38d26c49","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-07-04T03:22:45Z","title_canon_sha256":"fe0e768cf48878eecb7043f8370d75879d15d82ab91f92a654353b6e4152aed5"},"schema_version":"1.0","source":{"id":"1607.00730","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1607.00730","created_at":"2026-05-18T00:29:01Z"},{"alias_kind":"arxiv_version","alias_value":"1607.00730v4","created_at":"2026-05-18T00:29:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1607.00730","created_at":"2026-05-18T00:29:01Z"},{"alias_kind":"pith_short_12","alias_value":"SWTX4E65HZAK","created_at":"2026-05-18T12:30:44Z"},{"alias_kind":"pith_short_16","alias_value":"SWTX4E65HZAKCUQX","created_at":"2026-05-18T12:30:44Z"},{"alias_kind":"pith_short_8","alias_value":"SWTX4E65","created_at":"2026-05-18T12:30:44Z"}],"graph_snapshots":[{"event_id":"sha256:8259a2a048a1230855c3a3a62c060719ca4d3ef8c099d2cdd4d537bac0b6e6e5","target":"graph","created_at":"2026-05-18T00:29:01Z","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"},"paper":{"abstract_excerpt":"Estimating depth from a single RGB image is an ill-posed and inherently ambiguous problem. State-of-the-art deep learning methods can now estimate accurate 2D depth maps, but when the maps are projected into 3D, they lack local detail and are often highly distorted. We propose a fast-to-train two-streamed CNN that predicts depth and depth gradients, which are then fused together into an accurate and detailed depth map. We also define a novel set loss over multiple images; by regularizing the estimation between a common set of images, the network is less prone to over-fitting and achieves bette","authors_text":"Angela Yao, Jun Li, Reinhard Klein","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-07-04T03:22:45Z","title":"A Two-Streamed Network for Estimating Fine-Scaled Depth Maps from Single RGB Images"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1607.00730","kind":"arxiv","version":4},"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:b4af1ce4f145f0c74c2bff0db52ab1b89a69307b5fcd59ab4605a99857d860ca","target":"record","created_at":"2026-05-18T00:29:01Z","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":"42f38acbe0f4f2dc70bc82fc09b2d265799e087c4c91a6599f529d1d38d26c49","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-07-04T03:22:45Z","title_canon_sha256":"fe0e768cf48878eecb7043f8370d75879d15d82ab91f92a654353b6e4152aed5"},"schema_version":"1.0","source":{"id":"1607.00730","kind":"arxiv","version":4}},"canonical_sha256":"95a77e13dd3e40a15217d3cad6bbddfa047ddbde3a8d6677ccae5dc45d5c062d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"95a77e13dd3e40a15217d3cad6bbddfa047ddbde3a8d6677ccae5dc45d5c062d","first_computed_at":"2026-05-18T00:29:01.926724Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:29:01.926724Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"wtg+/pykS7D9F3QCuSp6GoR39yT9hcDccMFzQMOZoHK6jzP1AyndyhQjjl6wNSLh+ZI4kgF0tRiUjqP2h/l2BA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:29:01.928033Z","signed_message":"canonical_sha256_bytes"},"source_id":"1607.00730","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b4af1ce4f145f0c74c2bff0db52ab1b89a69307b5fcd59ab4605a99857d860ca","sha256:8259a2a048a1230855c3a3a62c060719ca4d3ef8c099d2cdd4d537bac0b6e6e5"],"state_sha256":"78d7c537607d70f451631f7b62475cd179f09dacb6906e3d105b0b50b91d7de0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/qDOYHN1D6Ob3B4NhV2nj3574Kwu1EYDk7jg82l+FGgxcXJNljne5kGIlnO5eUr0j1WX5R2S/KgI9OfNw+vgDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-20T15:16:12.684297Z","bundle_sha256":"c720dbfd934c3e12afcc027bc26d525eb6d0f711641e18b25601dfbd5d351e75"}}