{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:UEWSO2Q4HYACM3EVEEI67RGOQQ","short_pith_number":"pith:UEWSO2Q4","canonical_record":{"source":{"id":"2109.09628","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-09-20T15:28:36Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"1a03e84acc2f2ae6cd66d76abb42324a43f3fce0aace809cc19bbb58cd9e04a5","abstract_canon_sha256":"9446015f0d6dfabf709b50e79c12bca8d1b75cc4424d0861a5adbad19a6a995f"},"schema_version":"1.0"},"canonical_sha256":"a12d276a1c3e00266c952111efc4ce841fa85848c83f79619a104f61251500ed","source":{"kind":"arxiv","id":"2109.09628","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.09628","created_at":"2026-07-05T03:35:50Z"},{"alias_kind":"arxiv_version","alias_value":"2109.09628v4","created_at":"2026-07-05T03:35:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.09628","created_at":"2026-07-05T03:35:50Z"},{"alias_kind":"pith_short_12","alias_value":"UEWSO2Q4HYAC","created_at":"2026-07-05T03:35:50Z"},{"alias_kind":"pith_short_16","alias_value":"UEWSO2Q4HYACM3EV","created_at":"2026-07-05T03:35:50Z"},{"alias_kind":"pith_short_8","alias_value":"UEWSO2Q4","created_at":"2026-07-05T03:35:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:UEWSO2Q4HYACM3EVEEI67RGOQQ","target":"record","payload":{"canonical_record":{"source":{"id":"2109.09628","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-09-20T15:28:36Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"1a03e84acc2f2ae6cd66d76abb42324a43f3fce0aace809cc19bbb58cd9e04a5","abstract_canon_sha256":"9446015f0d6dfabf709b50e79c12bca8d1b75cc4424d0861a5adbad19a6a995f"},"schema_version":"1.0"},"canonical_sha256":"a12d276a1c3e00266c952111efc4ce841fa85848c83f79619a104f61251500ed","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:35:50.662128Z","signature_b64":"k/0BHHCmNCYaJ1uYhtqsVkP5iGp7qHrcd2OXytVv17stnmP64Rpz7NOixOVUtkEfN5dZUvyy1dru659Qt7epCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a12d276a1c3e00266c952111efc4ce841fa85848c83f79619a104f61251500ed","last_reissued_at":"2026-07-05T03:35:50.661614Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:35:50.661614Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2109.09628","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":"2026-07-05T03:35:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"F8RAz0uFE9jtyG3akKsyoJVPNuF3CP1+4kL6hxkstpSjy2479GwFnbZIDOLAstAaUh1Y+wbCWHTQbauoO2HMAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T18:43:38.851851Z"},"content_sha256":"e8ed3d5101e929629740974bb67e5638dd52772cecfb70b0faeddad37bd679bb","schema_version":"1.0","event_id":"sha256:e8ed3d5101e929629740974bb67e5638dd52772cecfb70b0faeddad37bd679bb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:UEWSO2Q4HYACM3EVEEI67RGOQQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Bing Li, Longlong Jing, Peng Yin, Yingli Tian, Ziyue Feng","submitted_at":"2021-09-20T15:28:36Z","abstract_excerpt":"Self-supervised monocular depth prediction provides a cost-effective solution to obtain the 3D location of each pixel. However, the existing approaches usually lead to unsatisfactory accuracy, which is critical for autonomous robots. In this paper, we propose FusionDepth, a novel two-stage network to advance the self-supervised monocular dense depth learning by leveraging low-cost sparse (e.g. 4-beam) LiDAR. Unlike the existing methods that use sparse LiDAR mainly in a manner of time-consuming iterative post-processing, our model fuses monocular image features and sparse LiDAR features to pred"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.09628","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2109.09628/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":"2026-07-05T03:35:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZAH68w6HdhWZPuWNNv30lEkrCFzSim2O+Qx+t9ZjqLaum6r7qq0ycPLge4ov9nGC0rwo/AQQMBNOtytnaHaBCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T18:43:38.852357Z"},"content_sha256":"508dd35d42a15e695266a66192aa5ded2800f5c51277e2442c61c971f596f975","schema_version":"1.0","event_id":"sha256:508dd35d42a15e695266a66192aa5ded2800f5c51277e2442c61c971f596f975"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UEWSO2Q4HYACM3EVEEI67RGOQQ/bundle.json","state_url":"https://pith.science/pith/UEWSO2Q4HYACM3EVEEI67RGOQQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UEWSO2Q4HYACM3EVEEI67RGOQQ/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":"2026-08-23T18:43:38Z","links":{"resolver":"https://pith.science/pith/UEWSO2Q4HYACM3EVEEI67RGOQQ","bundle":"https://pith.science/pith/UEWSO2Q4HYACM3EVEEI67RGOQQ/bundle.json","state":"https://pith.science/pith/UEWSO2Q4HYACM3EVEEI67RGOQQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UEWSO2Q4HYACM3EVEEI67RGOQQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:UEWSO2Q4HYACM3EVEEI67RGOQQ","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":"9446015f0d6dfabf709b50e79c12bca8d1b75cc4424d0861a5adbad19a6a995f","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-09-20T15:28:36Z","title_canon_sha256":"1a03e84acc2f2ae6cd66d76abb42324a43f3fce0aace809cc19bbb58cd9e04a5"},"schema_version":"1.0","source":{"id":"2109.09628","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.09628","created_at":"2026-07-05T03:35:50Z"},{"alias_kind":"arxiv_version","alias_value":"2109.09628v4","created_at":"2026-07-05T03:35:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.09628","created_at":"2026-07-05T03:35:50Z"},{"alias_kind":"pith_short_12","alias_value":"UEWSO2Q4HYAC","created_at":"2026-07-05T03:35:50Z"},{"alias_kind":"pith_short_16","alias_value":"UEWSO2Q4HYACM3EV","created_at":"2026-07-05T03:35:50Z"},{"alias_kind":"pith_short_8","alias_value":"UEWSO2Q4","created_at":"2026-07-05T03:35:50Z"}],"graph_snapshots":[{"event_id":"sha256:508dd35d42a15e695266a66192aa5ded2800f5c51277e2442c61c971f596f975","target":"graph","created_at":"2026-07-05T03:35:50Z","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/2109.09628/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Self-supervised monocular depth prediction provides a cost-effective solution to obtain the 3D location of each pixel. However, the existing approaches usually lead to unsatisfactory accuracy, which is critical for autonomous robots. In this paper, we propose FusionDepth, a novel two-stage network to advance the self-supervised monocular dense depth learning by leveraging low-cost sparse (e.g. 4-beam) LiDAR. Unlike the existing methods that use sparse LiDAR mainly in a manner of time-consuming iterative post-processing, our model fuses monocular image features and sparse LiDAR features to pred","authors_text":"Bing Li, Longlong Jing, Peng Yin, Yingli Tian, Ziyue Feng","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-09-20T15:28:36Z","title":"Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.09628","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:e8ed3d5101e929629740974bb67e5638dd52772cecfb70b0faeddad37bd679bb","target":"record","created_at":"2026-07-05T03:35:50Z","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":"9446015f0d6dfabf709b50e79c12bca8d1b75cc4424d0861a5adbad19a6a995f","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-09-20T15:28:36Z","title_canon_sha256":"1a03e84acc2f2ae6cd66d76abb42324a43f3fce0aace809cc19bbb58cd9e04a5"},"schema_version":"1.0","source":{"id":"2109.09628","kind":"arxiv","version":4}},"canonical_sha256":"a12d276a1c3e00266c952111efc4ce841fa85848c83f79619a104f61251500ed","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a12d276a1c3e00266c952111efc4ce841fa85848c83f79619a104f61251500ed","first_computed_at":"2026-07-05T03:35:50.661614Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:35:50.661614Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"k/0BHHCmNCYaJ1uYhtqsVkP5iGp7qHrcd2OXytVv17stnmP64Rpz7NOixOVUtkEfN5dZUvyy1dru659Qt7epCw==","signature_status":"signed_v1","signed_at":"2026-07-05T03:35:50.662128Z","signed_message":"canonical_sha256_bytes"},"source_id":"2109.09628","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e8ed3d5101e929629740974bb67e5638dd52772cecfb70b0faeddad37bd679bb","sha256:508dd35d42a15e695266a66192aa5ded2800f5c51277e2442c61c971f596f975"],"state_sha256":"5807b79b4d00eebc94d5bfb78e62ca034722437882e8587c3bd1291b48a84d09"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"R9qmOAN8vEQ5uhAMhxJqc9Co3wLSXwedNCIHaB0GOJtgZg5c2rt6bl19xUPS1uZvS6PEhLA0AsEx13NrtwAxDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-23T18:43:38.856872Z","bundle_sha256":"bdaf623fd7be2ab007cf681b0f003816c4550a1dfc5f980a1feec4fab2d11fce"}}