{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:FVS6VREN2OQIMPZBLMALSXW52K","short_pith_number":"pith:FVS6VREN","canonical_record":{"source":{"id":"1709.07492","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2017-09-21T18:50:04Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"f2047e2c6dc543abfcd02088a2532402eb55c073591f4e71d743cb272be918fd","abstract_canon_sha256":"4e6cc065e227fa8c2558a3f185f58a28a33f8e4bc5bd4cff5fcdb559104511b8"},"schema_version":"1.0"},"canonical_sha256":"2d65eac48dd3a0863f215b00b95eddd282f4c84ad3e5fc9b27055dc9eb95a7c0","source":{"kind":"arxiv","id":"1709.07492","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1709.07492","created_at":"2026-05-18T00:22:39Z"},{"alias_kind":"arxiv_version","alias_value":"1709.07492v2","created_at":"2026-05-18T00:22:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.07492","created_at":"2026-05-18T00:22:39Z"},{"alias_kind":"pith_short_12","alias_value":"FVS6VREN2OQI","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_16","alias_value":"FVS6VREN2OQIMPZB","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_8","alias_value":"FVS6VREN","created_at":"2026-05-18T12:31:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:FVS6VREN2OQIMPZBLMALSXW52K","target":"record","payload":{"canonical_record":{"source":{"id":"1709.07492","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2017-09-21T18:50:04Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"f2047e2c6dc543abfcd02088a2532402eb55c073591f4e71d743cb272be918fd","abstract_canon_sha256":"4e6cc065e227fa8c2558a3f185f58a28a33f8e4bc5bd4cff5fcdb559104511b8"},"schema_version":"1.0"},"canonical_sha256":"2d65eac48dd3a0863f215b00b95eddd282f4c84ad3e5fc9b27055dc9eb95a7c0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:22:39.512866Z","signature_b64":"zlSmlVcI61G77ypJx6RBEBebPvySiGo5Zu2AR1NoEC0o+Fu9W/8tbU6Gk2krpn1llVe5nYe9hGS+ET02qwEmAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2d65eac48dd3a0863f215b00b95eddd282f4c84ad3e5fc9b27055dc9eb95a7c0","last_reissued_at":"2026-05-18T00:22:39.512466Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:22:39.512466Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1709.07492","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:22:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2e2g+Jx+fknUhg8FfYMSpN5aRNUKJqzur2VUQeo2D0cdJhcl35lYNyNxC/JqOhWnRMCIulX1eSSw0XAxxoXSCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T16:04:15.497287Z"},"content_sha256":"d496228614e4da8158ba4edf97516419a92a9f17be8fee5e07757773827ac338","schema_version":"1.0","event_id":"sha256:d496228614e4da8158ba4edf97516419a92a9f17be8fee5e07757773827ac338"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:FVS6VREN2OQIMPZBLMALSXW52K","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.RO","authors_text":"Fangchang Ma, Sertac Karaman","submitted_at":"2017-09-21T18:50:04Z","abstract_excerpt":"We consider the problem of dense depth prediction from a sparse set of depth measurements and a single RGB image. Since depth estimation from monocular images alone is inherently ambiguous and unreliable, to attain a higher level of robustness and accuracy, we introduce additional sparse depth samples, which are either acquired with a low-resolution depth sensor or computed via visual Simultaneous Localization and Mapping (SLAM) algorithms. We propose the use of a single deep regression network to learn directly from the RGB-D raw data, and explore the impact of number of depth samples on pred"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.07492","kind":"arxiv","version":2},"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":"2026-05-18T00:22:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8iJTmRNg40/2+8z29ZCL3LP5PUYs51kTiApk0/+LuwmfbGcqUNx5VliZJ1LBWYXAvAfxgmqwmmFOf6tpovQLAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T16:04:15.497963Z"},"content_sha256":"2f7218ff4398b127073813858a0b42b20bd586ae855ce38e66fbd7c935dc4b74","schema_version":"1.0","event_id":"sha256:2f7218ff4398b127073813858a0b42b20bd586ae855ce38e66fbd7c935dc4b74"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FVS6VREN2OQIMPZBLMALSXW52K/bundle.json","state_url":"https://pith.science/pith/FVS6VREN2OQIMPZBLMALSXW52K/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FVS6VREN2OQIMPZBLMALSXW52K/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":"2026-05-26T16:04:15Z","links":{"resolver":"https://pith.science/pith/FVS6VREN2OQIMPZBLMALSXW52K","bundle":"https://pith.science/pith/FVS6VREN2OQIMPZBLMALSXW52K/bundle.json","state":"https://pith.science/pith/FVS6VREN2OQIMPZBLMALSXW52K/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FVS6VREN2OQIMPZBLMALSXW52K/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:FVS6VREN2OQIMPZBLMALSXW52K","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":"4e6cc065e227fa8c2558a3f185f58a28a33f8e4bc5bd4cff5fcdb559104511b8","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2017-09-21T18:50:04Z","title_canon_sha256":"f2047e2c6dc543abfcd02088a2532402eb55c073591f4e71d743cb272be918fd"},"schema_version":"1.0","source":{"id":"1709.07492","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1709.07492","created_at":"2026-05-18T00:22:39Z"},{"alias_kind":"arxiv_version","alias_value":"1709.07492v2","created_at":"2026-05-18T00:22:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.07492","created_at":"2026-05-18T00:22:39Z"},{"alias_kind":"pith_short_12","alias_value":"FVS6VREN2OQI","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_16","alias_value":"FVS6VREN2OQIMPZB","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_8","alias_value":"FVS6VREN","created_at":"2026-05-18T12:31:15Z"}],"graph_snapshots":[{"event_id":"sha256:2f7218ff4398b127073813858a0b42b20bd586ae855ce38e66fbd7c935dc4b74","target":"graph","created_at":"2026-05-18T00:22:39Z","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":"We consider the problem of dense depth prediction from a sparse set of depth measurements and a single RGB image. Since depth estimation from monocular images alone is inherently ambiguous and unreliable, to attain a higher level of robustness and accuracy, we introduce additional sparse depth samples, which are either acquired with a low-resolution depth sensor or computed via visual Simultaneous Localization and Mapping (SLAM) algorithms. We propose the use of a single deep regression network to learn directly from the RGB-D raw data, and explore the impact of number of depth samples on pred","authors_text":"Fangchang Ma, Sertac Karaman","cross_cats":["cs.AI","cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2017-09-21T18:50:04Z","title":"Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.07492","kind":"arxiv","version":2},"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:d496228614e4da8158ba4edf97516419a92a9f17be8fee5e07757773827ac338","target":"record","created_at":"2026-05-18T00:22:39Z","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":"4e6cc065e227fa8c2558a3f185f58a28a33f8e4bc5bd4cff5fcdb559104511b8","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2017-09-21T18:50:04Z","title_canon_sha256":"f2047e2c6dc543abfcd02088a2532402eb55c073591f4e71d743cb272be918fd"},"schema_version":"1.0","source":{"id":"1709.07492","kind":"arxiv","version":2}},"canonical_sha256":"2d65eac48dd3a0863f215b00b95eddd282f4c84ad3e5fc9b27055dc9eb95a7c0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2d65eac48dd3a0863f215b00b95eddd282f4c84ad3e5fc9b27055dc9eb95a7c0","first_computed_at":"2026-05-18T00:22:39.512466Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:22:39.512466Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zlSmlVcI61G77ypJx6RBEBebPvySiGo5Zu2AR1NoEC0o+Fu9W/8tbU6Gk2krpn1llVe5nYe9hGS+ET02qwEmAQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:22:39.512866Z","signed_message":"canonical_sha256_bytes"},"source_id":"1709.07492","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d496228614e4da8158ba4edf97516419a92a9f17be8fee5e07757773827ac338","sha256:2f7218ff4398b127073813858a0b42b20bd586ae855ce38e66fbd7c935dc4b74"],"state_sha256":"e0909c916361d979323fc08425bc470ee117830ddc887c15763d7b705002db99"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nhC6vv4kazNW6tQO9dKwvn+zupLNNHgAYhc/yTB1XhlsGU2Dq7NHjat70F02ccUzKjPDyGcIkn+rzbe5QsTICQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T16:04:15.501723Z","bundle_sha256":"838f0e21aa6a29acb36224f5c84e5ab0a0e6b53f33ef9848e640060e76ba8a3c"}}