{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:I4QM2AKDHT4WABRDRGBMBC3736","short_pith_number":"pith:I4QM2AKD","canonical_record":{"source":{"id":"2312.01283","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-03T04:55:32Z","cross_cats_sorted":[],"title_canon_sha256":"312075077c361e39be9e0bf12dc38701c1c218b31a3ccd2bdde3a60a4b1dc17d","abstract_canon_sha256":"7bdf5a5e7512bca71393afe11bd895919d42addd37382b9b6af042d945aeaff0"},"schema_version":"1.0"},"canonical_sha256":"4720cd01433cf96006238982c08b7fdf9abdf5d47fe7b4ded0fb579cccf8baf9","source":{"kind":"arxiv","id":"2312.01283","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.01283","created_at":"2026-07-05T07:19:32Z"},{"alias_kind":"arxiv_version","alias_value":"2312.01283v1","created_at":"2026-07-05T07:19:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.01283","created_at":"2026-07-05T07:19:32Z"},{"alias_kind":"pith_short_12","alias_value":"I4QM2AKDHT4W","created_at":"2026-07-05T07:19:32Z"},{"alias_kind":"pith_short_16","alias_value":"I4QM2AKDHT4WABRD","created_at":"2026-07-05T07:19:32Z"},{"alias_kind":"pith_short_8","alias_value":"I4QM2AKD","created_at":"2026-07-05T07:19:32Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:I4QM2AKDHT4WABRDRGBMBC3736","target":"record","payload":{"canonical_record":{"source":{"id":"2312.01283","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-03T04:55:32Z","cross_cats_sorted":[],"title_canon_sha256":"312075077c361e39be9e0bf12dc38701c1c218b31a3ccd2bdde3a60a4b1dc17d","abstract_canon_sha256":"7bdf5a5e7512bca71393afe11bd895919d42addd37382b9b6af042d945aeaff0"},"schema_version":"1.0"},"canonical_sha256":"4720cd01433cf96006238982c08b7fdf9abdf5d47fe7b4ded0fb579cccf8baf9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:19:32.526912Z","signature_b64":"izqCsw1NelrCR52JwgPJLdI2VODhQDmc8kmkoYVTjIAWlle6T3OnmEDrx5qDmVGKKPL1XCLACOYR2U2hlVkWCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4720cd01433cf96006238982c08b7fdf9abdf5d47fe7b4ded0fb579cccf8baf9","last_reissued_at":"2026-07-05T07:19:32.526411Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:19:32.526411Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2312.01283","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":"2026-07-05T07:19:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fultNmZDKg0ta832ep97AaOi+5hSM8HO23Q4s7QOui65YYc+/HlxFzMq9Rs0lX6AcwVFbAaUmdD2lUxBeNnECg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-02T20:42:38.281586Z"},"content_sha256":"93b5776332ddbfa114068b5598fcf4ccb9650bb42baaf61e1e0ed43ef8f84b6f","schema_version":"1.0","event_id":"sha256:93b5776332ddbfa114068b5598fcf4ccb9650bb42baaf61e1e0ed43ef8f84b6f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:I4QM2AKDHT4WABRDRGBMBC3736","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deeper into Self-Supervised Monocular Indoor Depth Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chao Fan, Feiqing Zhang, Yue Li, ZhenYu Yin","submitted_at":"2023-12-03T04:55:32Z","abstract_excerpt":"Monocular depth estimation using Convolutional Neural Networks (CNNs) has shown impressive performance in outdoor driving scenes. However, self-supervised learning of indoor depth from monocular sequences is quite challenging for researchers because of the following two main reasons. One is the large areas of low-texture regions and the other is the complex ego-motion on indoor training datasets. In this work, our proposed method, named IndoorDepth, consists of two innovations. In particular, we first propose a novel photometric loss with improved structural similarity (SSIM) function to tackl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.01283","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2312.01283/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":"2026-07-05T07:19:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8fNJuxrs85Dcnudm5JrrxYYncM+RCQ3dr8fZ8ozopXuXsp33TJTmgu6panDOXl3pJlGi9F+BIavoH2pX+Y4aBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-02T20:42:38.282101Z"},"content_sha256":"6c41a145a6d8c41502739f7312f7de856b4f250ffa7aea7143330a7ca0834f07","schema_version":"1.0","event_id":"sha256:6c41a145a6d8c41502739f7312f7de856b4f250ffa7aea7143330a7ca0834f07"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/I4QM2AKDHT4WABRDRGBMBC3736/bundle.json","state_url":"https://pith.science/pith/I4QM2AKDHT4WABRDRGBMBC3736/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/I4QM2AKDHT4WABRDRGBMBC3736/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":"2026-08-02T20:42:38Z","links":{"resolver":"https://pith.science/pith/I4QM2AKDHT4WABRDRGBMBC3736","bundle":"https://pith.science/pith/I4QM2AKDHT4WABRDRGBMBC3736/bundle.json","state":"https://pith.science/pith/I4QM2AKDHT4WABRDRGBMBC3736/state.json","well_known_bundle":"https://pith.science/.well-known/pith/I4QM2AKDHT4WABRDRGBMBC3736/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:I4QM2AKDHT4WABRDRGBMBC3736","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":"7bdf5a5e7512bca71393afe11bd895919d42addd37382b9b6af042d945aeaff0","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-03T04:55:32Z","title_canon_sha256":"312075077c361e39be9e0bf12dc38701c1c218b31a3ccd2bdde3a60a4b1dc17d"},"schema_version":"1.0","source":{"id":"2312.01283","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.01283","created_at":"2026-07-05T07:19:32Z"},{"alias_kind":"arxiv_version","alias_value":"2312.01283v1","created_at":"2026-07-05T07:19:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.01283","created_at":"2026-07-05T07:19:32Z"},{"alias_kind":"pith_short_12","alias_value":"I4QM2AKDHT4W","created_at":"2026-07-05T07:19:32Z"},{"alias_kind":"pith_short_16","alias_value":"I4QM2AKDHT4WABRD","created_at":"2026-07-05T07:19:32Z"},{"alias_kind":"pith_short_8","alias_value":"I4QM2AKD","created_at":"2026-07-05T07:19:32Z"}],"graph_snapshots":[{"event_id":"sha256:6c41a145a6d8c41502739f7312f7de856b4f250ffa7aea7143330a7ca0834f07","target":"graph","created_at":"2026-07-05T07:19:32Z","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/2312.01283/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Monocular depth estimation using Convolutional Neural Networks (CNNs) has shown impressive performance in outdoor driving scenes. However, self-supervised learning of indoor depth from monocular sequences is quite challenging for researchers because of the following two main reasons. One is the large areas of low-texture regions and the other is the complex ego-motion on indoor training datasets. In this work, our proposed method, named IndoorDepth, consists of two innovations. In particular, we first propose a novel photometric loss with improved structural similarity (SSIM) function to tackl","authors_text":"Chao Fan, Feiqing Zhang, Yue Li, ZhenYu Yin","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-03T04:55:32Z","title":"Deeper into Self-Supervised Monocular Indoor Depth Estimation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.01283","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":"sha256:93b5776332ddbfa114068b5598fcf4ccb9650bb42baaf61e1e0ed43ef8f84b6f","target":"record","created_at":"2026-07-05T07:19:32Z","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":"7bdf5a5e7512bca71393afe11bd895919d42addd37382b9b6af042d945aeaff0","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-03T04:55:32Z","title_canon_sha256":"312075077c361e39be9e0bf12dc38701c1c218b31a3ccd2bdde3a60a4b1dc17d"},"schema_version":"1.0","source":{"id":"2312.01283","kind":"arxiv","version":1}},"canonical_sha256":"4720cd01433cf96006238982c08b7fdf9abdf5d47fe7b4ded0fb579cccf8baf9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4720cd01433cf96006238982c08b7fdf9abdf5d47fe7b4ded0fb579cccf8baf9","first_computed_at":"2026-07-05T07:19:32.526411Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:19:32.526411Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"izqCsw1NelrCR52JwgPJLdI2VODhQDmc8kmkoYVTjIAWlle6T3OnmEDrx5qDmVGKKPL1XCLACOYR2U2hlVkWCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:19:32.526912Z","signed_message":"canonical_sha256_bytes"},"source_id":"2312.01283","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:93b5776332ddbfa114068b5598fcf4ccb9650bb42baaf61e1e0ed43ef8f84b6f","sha256:6c41a145a6d8c41502739f7312f7de856b4f250ffa7aea7143330a7ca0834f07"],"state_sha256":"00b27c44241c7a7136b848baa6e825664041e79309d80e621882f64851013791"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FCv1WGwxOYEAp6oAXmuHdcdXIz83qVQf/uXwlZ6rn+FFdXH6/cAxGJ3Do0hTQz7SeB63f544oPYj16vQp8N7AA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-02T20:42:38.287670Z","bundle_sha256":"f03e1e66d800597e9c1b5697f5d344aed5490b58ab21300642d1a15df98b5355"}}