{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:I2V2CXR3SQZ4WJQW66ULL5VMVV","short_pith_number":"pith:I2V2CXR3","canonical_record":{"source":{"id":"1702.02706","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-02-09T05:08:22Z","cross_cats_sorted":[],"title_canon_sha256":"7b9d7566ea08209dedd9a3727cb2775efa615240edc04bce74af1085e1ecf294","abstract_canon_sha256":"c3b95ae629054fa147120ae56b0a24e45854eaaab0aff94a9340bc5ddd8cca2e"},"schema_version":"1.0"},"canonical_sha256":"46aba15e3b9433cb2616f7a8b5f6acad58ec3743beef3757773c99231768739c","source":{"kind":"arxiv","id":"1702.02706","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1702.02706","created_at":"2026-05-18T00:44:38Z"},{"alias_kind":"arxiv_version","alias_value":"1702.02706v3","created_at":"2026-05-18T00:44:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1702.02706","created_at":"2026-05-18T00:44:38Z"},{"alias_kind":"pith_short_12","alias_value":"I2V2CXR3SQZ4","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_16","alias_value":"I2V2CXR3SQZ4WJQW","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_8","alias_value":"I2V2CXR3","created_at":"2026-05-18T12:31:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:I2V2CXR3SQZ4WJQW66ULL5VMVV","target":"record","payload":{"canonical_record":{"source":{"id":"1702.02706","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-02-09T05:08:22Z","cross_cats_sorted":[],"title_canon_sha256":"7b9d7566ea08209dedd9a3727cb2775efa615240edc04bce74af1085e1ecf294","abstract_canon_sha256":"c3b95ae629054fa147120ae56b0a24e45854eaaab0aff94a9340bc5ddd8cca2e"},"schema_version":"1.0"},"canonical_sha256":"46aba15e3b9433cb2616f7a8b5f6acad58ec3743beef3757773c99231768739c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:44:38.559238Z","signature_b64":"2fqyYR1D8MTGRROVB+IIOjHk/AcZ2lNdVyLgStgpEhio32sg5z+c6yDcI3QqGlwjnIR0I4i5tV0RUcvElPN4AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"46aba15e3b9433cb2616f7a8b5f6acad58ec3743beef3757773c99231768739c","last_reissued_at":"2026-05-18T00:44:38.558825Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:44:38.558825Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1702.02706","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":"2026-05-18T00:44:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oKtWCaMrnqOrwETgLBHi+iFRuzXaA0Tia+F63rR1PIJWybFvBw8gYfn57W2FkRPmXn6TmNinToF1H8U5DeppAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T13:34:25.636695Z"},"content_sha256":"a17b5a254b33256589dd2ac96cefb2bf558bd265b15f28c1f599e3d053835157","schema_version":"1.0","event_id":"sha256:a17b5a254b33256589dd2ac96cefb2bf558bd265b15f28c1f599e3d053835157"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:I2V2CXR3SQZ4WJQW66ULL5VMVV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Semi-Supervised Deep Learning for Monocular Depth Map Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bastian Leibe, J\\\"org St\\\"uckler, Yevhen Kuznietsov","submitted_at":"2017-02-09T05:08:22Z","abstract_excerpt":"Supervised deep learning often suffers from the lack of sufficient training data. Specifically in the context of monocular depth map prediction, it is barely possible to determine dense ground truth depth images in realistic dynamic outdoor environments. When using LiDAR sensors, for instance, noise is present in the distance measurements, the calibration between sensors cannot be perfect, and the measurements are typically much sparser than the camera images. In this paper, we propose a novel approach to depth map prediction from monocular images that learns in a semi-supervised way. While we"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1702.02706","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":""},"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":"2026-05-18T00:44:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cy1se9iDFVY8b7SqYKvX48OM+w+e792DFoD/46byft3wZ3SEkM7Y+ET7LL5pFFvn4OeWabxr1KuVKcvd72+UAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T13:34:25.637438Z"},"content_sha256":"7f97a946f01422f216abbd9a62f3e3590715c545f3754b1af5282791091fb330","schema_version":"1.0","event_id":"sha256:7f97a946f01422f216abbd9a62f3e3590715c545f3754b1af5282791091fb330"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/I2V2CXR3SQZ4WJQW66ULL5VMVV/bundle.json","state_url":"https://pith.science/pith/I2V2CXR3SQZ4WJQW66ULL5VMVV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/I2V2CXR3SQZ4WJQW66ULL5VMVV/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":"2026-05-31T13:34:25Z","links":{"resolver":"https://pith.science/pith/I2V2CXR3SQZ4WJQW66ULL5VMVV","bundle":"https://pith.science/pith/I2V2CXR3SQZ4WJQW66ULL5VMVV/bundle.json","state":"https://pith.science/pith/I2V2CXR3SQZ4WJQW66ULL5VMVV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/I2V2CXR3SQZ4WJQW66ULL5VMVV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:I2V2CXR3SQZ4WJQW66ULL5VMVV","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":"c3b95ae629054fa147120ae56b0a24e45854eaaab0aff94a9340bc5ddd8cca2e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-02-09T05:08:22Z","title_canon_sha256":"7b9d7566ea08209dedd9a3727cb2775efa615240edc04bce74af1085e1ecf294"},"schema_version":"1.0","source":{"id":"1702.02706","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1702.02706","created_at":"2026-05-18T00:44:38Z"},{"alias_kind":"arxiv_version","alias_value":"1702.02706v3","created_at":"2026-05-18T00:44:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1702.02706","created_at":"2026-05-18T00:44:38Z"},{"alias_kind":"pith_short_12","alias_value":"I2V2CXR3SQZ4","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_16","alias_value":"I2V2CXR3SQZ4WJQW","created_at":"2026-05-18T12:31:21Z"},{"alias_kind":"pith_short_8","alias_value":"I2V2CXR3","created_at":"2026-05-18T12:31:21Z"}],"graph_snapshots":[{"event_id":"sha256:7f97a946f01422f216abbd9a62f3e3590715c545f3754b1af5282791091fb330","target":"graph","created_at":"2026-05-18T00:44:38Z","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":"Supervised deep learning often suffers from the lack of sufficient training data. Specifically in the context of monocular depth map prediction, it is barely possible to determine dense ground truth depth images in realistic dynamic outdoor environments. When using LiDAR sensors, for instance, noise is present in the distance measurements, the calibration between sensors cannot be perfect, and the measurements are typically much sparser than the camera images. In this paper, we propose a novel approach to depth map prediction from monocular images that learns in a semi-supervised way. While we","authors_text":"Bastian Leibe, J\\\"org St\\\"uckler, Yevhen Kuznietsov","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-02-09T05:08:22Z","title":"Semi-Supervised Deep Learning for Monocular Depth Map Prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1702.02706","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:a17b5a254b33256589dd2ac96cefb2bf558bd265b15f28c1f599e3d053835157","target":"record","created_at":"2026-05-18T00:44:38Z","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":"c3b95ae629054fa147120ae56b0a24e45854eaaab0aff94a9340bc5ddd8cca2e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-02-09T05:08:22Z","title_canon_sha256":"7b9d7566ea08209dedd9a3727cb2775efa615240edc04bce74af1085e1ecf294"},"schema_version":"1.0","source":{"id":"1702.02706","kind":"arxiv","version":3}},"canonical_sha256":"46aba15e3b9433cb2616f7a8b5f6acad58ec3743beef3757773c99231768739c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"46aba15e3b9433cb2616f7a8b5f6acad58ec3743beef3757773c99231768739c","first_computed_at":"2026-05-18T00:44:38.558825Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:44:38.558825Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2fqyYR1D8MTGRROVB+IIOjHk/AcZ2lNdVyLgStgpEhio32sg5z+c6yDcI3QqGlwjnIR0I4i5tV0RUcvElPN4AQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:44:38.559238Z","signed_message":"canonical_sha256_bytes"},"source_id":"1702.02706","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a17b5a254b33256589dd2ac96cefb2bf558bd265b15f28c1f599e3d053835157","sha256:7f97a946f01422f216abbd9a62f3e3590715c545f3754b1af5282791091fb330"],"state_sha256":"c17211ecfecd0f6081a514521411e814f2867f4aa20e085ddbe45a3881ff6fbd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YG6nmuJFWKEuiyh9LU1sjOY8oRQCQNEwHqM3M3IAd1GvdhdjzmCEtAZ6Pqn1TssQkx+4lhYG/N79OtOWw4ykBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T13:34:25.641197Z","bundle_sha256":"ce34809983b607985d40703aefd296e9b53309093c15f1afd72641b5387dabb2"}}