{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:7O473TTA3PKV725UJT2AMPN54H","short_pith_number":"pith:7O473TTA","canonical_record":{"source":{"id":"1708.02287","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-02T06:41:50Z","cross_cats_sorted":[],"title_canon_sha256":"fa8d135fc32e1a21b55400303e2e67815719e2aad63788fecf17a61dc1c4aee8","abstract_canon_sha256":"8caf946fa24e1021d2093c39de7a6d79c6c7a2321c3a30309ee53908486308f1"},"schema_version":"1.0"},"canonical_sha256":"fbb9fdce60dbd55febb44cf4063dbde1c13d1d08229aac2ff0fcd1fa78ee2a1e","source":{"kind":"arxiv","id":"1708.02287","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.02287","created_at":"2026-05-18T00:38:24Z"},{"alias_kind":"arxiv_version","alias_value":"1708.02287v1","created_at":"2026-05-18T00:38:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.02287","created_at":"2026-05-18T00:38:24Z"},{"alias_kind":"pith_short_12","alias_value":"7O473TTA3PKV","created_at":"2026-05-18T12:31:05Z"},{"alias_kind":"pith_short_16","alias_value":"7O473TTA3PKV725U","created_at":"2026-05-18T12:31:05Z"},{"alias_kind":"pith_short_8","alias_value":"7O473TTA","created_at":"2026-05-18T12:31:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:7O473TTA3PKV725UJT2AMPN54H","target":"record","payload":{"canonical_record":{"source":{"id":"1708.02287","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-02T06:41:50Z","cross_cats_sorted":[],"title_canon_sha256":"fa8d135fc32e1a21b55400303e2e67815719e2aad63788fecf17a61dc1c4aee8","abstract_canon_sha256":"8caf946fa24e1021d2093c39de7a6d79c6c7a2321c3a30309ee53908486308f1"},"schema_version":"1.0"},"canonical_sha256":"fbb9fdce60dbd55febb44cf4063dbde1c13d1d08229aac2ff0fcd1fa78ee2a1e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:38:24.858677Z","signature_b64":"lSlTGFI+Tg0Xj0JTQaeuyDzYV+WFiA6wOBuy+pz1S7JsFV8NlGONr9ROEEfimidlk8xV2H1DJ4zFJZTImEWmDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fbb9fdce60dbd55febb44cf4063dbde1c13d1d08229aac2ff0fcd1fa78ee2a1e","last_reissued_at":"2026-05-18T00:38:24.858012Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:38:24.858012Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1708.02287","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:38:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Qgpt9uS7sQLR18LByVEe5/OpwbJFy2arXGODhFNnygKla16iq2k2t6G1hp8WP/0f2e98KBxYca8oR5mbz5z7Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T00:40:21.154867Z"},"content_sha256":"07381dabea8d86084f46c87380bbc4d55a874bb9154477763285ca45ed9782c4","schema_version":"1.0","event_id":"sha256:07381dabea8d86084f46c87380bbc4d55a874bb9154477763285ca45ed9782c4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:7O473TTA3PKV725UJT2AMPN54H","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Monocular Depth Estimation with Hierarchical Fusion of Dilated CNNs and Soft-Weighted-Sum Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bo Li, Mingyi He, Yuchao Dai","submitted_at":"2017-08-02T06:41:50Z","abstract_excerpt":"Monocular depth estimation is a challenging task in complex compositions depicting multiple objects of diverse scales. Albeit the recent great progress thanks to the deep convolutional neural networks (CNNs), the state-of-the-art monocular depth estimation methods still fall short to handle such real-world challenging scenarios. In this paper, we propose a deep end-to-end learning framework to tackle these challenges, which learns the direct mapping from a color image to the corresponding depth map. First, we represent monocular depth estimation as a multi-category dense labeling task by contr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.02287","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:38:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dbPigYluP51ogvavxakTHUp1im/zoYG9TITIIZ+bKMdaDQ4wUCqHcv9fqYg1bS4Xenv+6LmilcUXS6lmU/xcAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T00:40:21.155556Z"},"content_sha256":"9996d8a993d03e0095252d138ab329ade5a187cde1d0abb4be591d37fe410656","schema_version":"1.0","event_id":"sha256:9996d8a993d03e0095252d138ab329ade5a187cde1d0abb4be591d37fe410656"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7O473TTA3PKV725UJT2AMPN54H/bundle.json","state_url":"https://pith.science/pith/7O473TTA3PKV725UJT2AMPN54H/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7O473TTA3PKV725UJT2AMPN54H/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":"2026-05-26T00:40:21Z","links":{"resolver":"https://pith.science/pith/7O473TTA3PKV725UJT2AMPN54H","bundle":"https://pith.science/pith/7O473TTA3PKV725UJT2AMPN54H/bundle.json","state":"https://pith.science/pith/7O473TTA3PKV725UJT2AMPN54H/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7O473TTA3PKV725UJT2AMPN54H/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:7O473TTA3PKV725UJT2AMPN54H","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":"8caf946fa24e1021d2093c39de7a6d79c6c7a2321c3a30309ee53908486308f1","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-02T06:41:50Z","title_canon_sha256":"fa8d135fc32e1a21b55400303e2e67815719e2aad63788fecf17a61dc1c4aee8"},"schema_version":"1.0","source":{"id":"1708.02287","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.02287","created_at":"2026-05-18T00:38:24Z"},{"alias_kind":"arxiv_version","alias_value":"1708.02287v1","created_at":"2026-05-18T00:38:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.02287","created_at":"2026-05-18T00:38:24Z"},{"alias_kind":"pith_short_12","alias_value":"7O473TTA3PKV","created_at":"2026-05-18T12:31:05Z"},{"alias_kind":"pith_short_16","alias_value":"7O473TTA3PKV725U","created_at":"2026-05-18T12:31:05Z"},{"alias_kind":"pith_short_8","alias_value":"7O473TTA","created_at":"2026-05-18T12:31:05Z"}],"graph_snapshots":[{"event_id":"sha256:9996d8a993d03e0095252d138ab329ade5a187cde1d0abb4be591d37fe410656","target":"graph","created_at":"2026-05-18T00:38:24Z","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":"Monocular depth estimation is a challenging task in complex compositions depicting multiple objects of diverse scales. Albeit the recent great progress thanks to the deep convolutional neural networks (CNNs), the state-of-the-art monocular depth estimation methods still fall short to handle such real-world challenging scenarios. In this paper, we propose a deep end-to-end learning framework to tackle these challenges, which learns the direct mapping from a color image to the corresponding depth map. First, we represent monocular depth estimation as a multi-category dense labeling task by contr","authors_text":"Bo Li, Mingyi He, Yuchao Dai","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-02T06:41:50Z","title":"Monocular Depth Estimation with Hierarchical Fusion of Dilated CNNs and Soft-Weighted-Sum Inference"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.02287","kind":"arxiv","version":1},"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":"sha256:07381dabea8d86084f46c87380bbc4d55a874bb9154477763285ca45ed9782c4","target":"record","created_at":"2026-05-18T00:38:24Z","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":"8caf946fa24e1021d2093c39de7a6d79c6c7a2321c3a30309ee53908486308f1","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-02T06:41:50Z","title_canon_sha256":"fa8d135fc32e1a21b55400303e2e67815719e2aad63788fecf17a61dc1c4aee8"},"schema_version":"1.0","source":{"id":"1708.02287","kind":"arxiv","version":1}},"canonical_sha256":"fbb9fdce60dbd55febb44cf4063dbde1c13d1d08229aac2ff0fcd1fa78ee2a1e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fbb9fdce60dbd55febb44cf4063dbde1c13d1d08229aac2ff0fcd1fa78ee2a1e","first_computed_at":"2026-05-18T00:38:24.858012Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:38:24.858012Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"lSlTGFI+Tg0Xj0JTQaeuyDzYV+WFiA6wOBuy+pz1S7JsFV8NlGONr9ROEEfimidlk8xV2H1DJ4zFJZTImEWmDA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:38:24.858677Z","signed_message":"canonical_sha256_bytes"},"source_id":"1708.02287","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:07381dabea8d86084f46c87380bbc4d55a874bb9154477763285ca45ed9782c4","sha256:9996d8a993d03e0095252d138ab329ade5a187cde1d0abb4be591d37fe410656"],"state_sha256":"76a2d6e824fe65778bddf0d0aa14749ddefe7a026865e5433b5c7231d52ebd08"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ifxoc2bUhG5gQccUwF19MNXJKjhGTvissNZ15AdbWcCYvL7Ms2m5olImfvgULG77sHNVw4sdbjpgPasRp0+fCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T00:40:21.159148Z","bundle_sha256":"4877db7c49fb1eaa8761bf7e40f85ee7100be81e970ca990334ee1062509b8e5"}}