{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:W6QJIVVHXUMDO6CHDQIU6K6EQS","short_pith_number":"pith:W6QJIVVH","schema_version":"1.0","canonical_sha256":"b7a09456a7bd183778471c114f2bc484bbd1e4968e1c87db24b5f70ded8fdc96","source":{"kind":"arxiv","id":"2206.11250","version":2},"attestation_state":"computed","paper":{"title":"Leveraging RGB-D Data with Cross-Modal Context Mining for Glass Surface Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jiaying Lin, Rynson W.H. Lau, Shuquan Ye, Yuen-Hei Yeung","submitted_at":"2022-06-22T17:56:09Z","abstract_excerpt":"Glass surfaces are becoming increasingly ubiquitous as modern buildings tend to use a lot of glass panels. This, however, poses substantial challenges to the operations of autonomous systems such as robots, self-driving cars, and drones, as these glass panels can become transparent obstacles to navigation. Existing works attempt to exploit various cues, including glass boundary context or reflections, as priors. However, they are all based on input RGB images. We observe that the transmission of 3D depth sensor light through glass surfaces often produces blank regions in the depth maps, which "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2206.11250","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-06-22T17:56:09Z","cross_cats_sorted":[],"title_canon_sha256":"8e671c13ff4c6ec14f817bf21bb72dc280fef7737700a930f7827e59616c1268","abstract_canon_sha256":"29766297982e40e2d2878bdd70ce151d626e70e7157ebbaeb133ea42822dbc25"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:49:39.284937Z","signature_b64":"lgAbpgcPvqwlWr3Pf84WZWGOawQmZtgaqqnx2zQrerl9vEVrgD760ra480TY+hWIKnnlsEaMJlacmqtQ498SBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b7a09456a7bd183778471c114f2bc484bbd1e4968e1c87db24b5f70ded8fdc96","last_reissued_at":"2026-07-05T09:49:39.284501Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:49:39.284501Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Leveraging RGB-D Data with Cross-Modal Context Mining for Glass Surface Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jiaying Lin, Rynson W.H. Lau, Shuquan Ye, Yuen-Hei Yeung","submitted_at":"2022-06-22T17:56:09Z","abstract_excerpt":"Glass surfaces are becoming increasingly ubiquitous as modern buildings tend to use a lot of glass panels. This, however, poses substantial challenges to the operations of autonomous systems such as robots, self-driving cars, and drones, as these glass panels can become transparent obstacles to navigation. Existing works attempt to exploit various cues, including glass boundary context or reflections, as priors. However, they are all based on input RGB images. We observe that the transmission of 3D depth sensor light through glass surfaces often produces blank regions in the depth maps, which "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.11250","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2206.11250/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2206.11250","created_at":"2026-07-05T09:49:39.284553+00:00"},{"alias_kind":"arxiv_version","alias_value":"2206.11250v2","created_at":"2026-07-05T09:49:39.284553+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.11250","created_at":"2026-07-05T09:49:39.284553+00:00"},{"alias_kind":"pith_short_12","alias_value":"W6QJIVVHXUMD","created_at":"2026-07-05T09:49:39.284553+00:00"},{"alias_kind":"pith_short_16","alias_value":"W6QJIVVHXUMDO6CH","created_at":"2026-07-05T09:49:39.284553+00:00"},{"alias_kind":"pith_short_8","alias_value":"W6QJIVVH","created_at":"2026-07-05T09:49:39.284553+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/W6QJIVVHXUMDO6CHDQIU6K6EQS","json":"https://pith.science/pith/W6QJIVVHXUMDO6CHDQIU6K6EQS.json","graph_json":"https://pith.science/api/pith-number/W6QJIVVHXUMDO6CHDQIU6K6EQS/graph.json","events_json":"https://pith.science/api/pith-number/W6QJIVVHXUMDO6CHDQIU6K6EQS/events.json","paper":"https://pith.science/paper/W6QJIVVH"},"agent_actions":{"view_html":"https://pith.science/pith/W6QJIVVHXUMDO6CHDQIU6K6EQS","download_json":"https://pith.science/pith/W6QJIVVHXUMDO6CHDQIU6K6EQS.json","view_paper":"https://pith.science/paper/W6QJIVVH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2206.11250&json=true","fetch_graph":"https://pith.science/api/pith-number/W6QJIVVHXUMDO6CHDQIU6K6EQS/graph.json","fetch_events":"https://pith.science/api/pith-number/W6QJIVVHXUMDO6CHDQIU6K6EQS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/W6QJIVVHXUMDO6CHDQIU6K6EQS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/W6QJIVVHXUMDO6CHDQIU6K6EQS/action/storage_attestation","attest_author":"https://pith.science/pith/W6QJIVVHXUMDO6CHDQIU6K6EQS/action/author_attestation","sign_citation":"https://pith.science/pith/W6QJIVVHXUMDO6CHDQIU6K6EQS/action/citation_signature","submit_replication":"https://pith.science/pith/W6QJIVVHXUMDO6CHDQIU6K6EQS/action/replication_record"}},"created_at":"2026-07-05T09:49:39.284553+00:00","updated_at":"2026-07-05T09:49:39.284553+00:00"}