{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:4EK4ZTV6N24G3CEVLOK44DRLWR","short_pith_number":"pith:4EK4ZTV6","schema_version":"1.0","canonical_sha256":"e115cccebe6eb86d88955b95ce0e2bb44765d6ce7f038b690486ebe11087699a","source":{"kind":"arxiv","id":"2306.03403","version":2},"attestation_state":"computed","paper":{"title":"SGAT4PASS: Spherical Geometry-Aware Transformer for PAnoramic Semantic Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.MM"],"primary_cat":"cs.CV","authors_text":"Gaoang Wang, Tao Wu, Xi Li, Xuewei Li, Ying Shan, Zhongang Qi","submitted_at":"2023-06-06T04:49:51Z","abstract_excerpt":"As an important and challenging problem in computer vision, PAnoramic Semantic Segmentation (PASS) gives complete scene perception based on an ultra-wide angle of view. Usually, prevalent PASS methods with 2D panoramic image input focus on solving image distortions but lack consideration of the 3D properties of original $360^{\\circ}$ data. Therefore, their performance will drop a lot when inputting panoramic images with the 3D disturbance. To be more robust to 3D disturbance, we propose our Spherical Geometry-Aware Transformer for PAnoramic Semantic Segmentation (SGAT4PASS), considering 3D sph"},"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":"2306.03403","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-06-06T04:49:51Z","cross_cats_sorted":["cs.AI","cs.LG","cs.MM"],"title_canon_sha256":"e0181eebf914d76356a365cc9f281a20c952a094be9da5a7a67f4e79d9b6b0ad","abstract_canon_sha256":"36b5ba8846ef1941df1168a6672ba5345e1c8a5ee8d5ef21c7568e1150d01eeb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:54:49.139340Z","signature_b64":"xUV5KDJpy7EojqY9He3jFWjfg5W2gxb339SytBLgXUrc+FbElM2XFpiBln3v0xT/6dZ0BSVWh6cA9k1Y22JtBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e115cccebe6eb86d88955b95ce0e2bb44765d6ce7f038b690486ebe11087699a","last_reissued_at":"2026-07-05T07:54:49.138899Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:54:49.138899Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SGAT4PASS: Spherical Geometry-Aware Transformer for PAnoramic Semantic Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.MM"],"primary_cat":"cs.CV","authors_text":"Gaoang Wang, Tao Wu, Xi Li, Xuewei Li, Ying Shan, Zhongang Qi","submitted_at":"2023-06-06T04:49:51Z","abstract_excerpt":"As an important and challenging problem in computer vision, PAnoramic Semantic Segmentation (PASS) gives complete scene perception based on an ultra-wide angle of view. Usually, prevalent PASS methods with 2D panoramic image input focus on solving image distortions but lack consideration of the 3D properties of original $360^{\\circ}$ data. Therefore, their performance will drop a lot when inputting panoramic images with the 3D disturbance. To be more robust to 3D disturbance, we propose our Spherical Geometry-Aware Transformer for PAnoramic Semantic Segmentation (SGAT4PASS), considering 3D sph"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.03403","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/2306.03403/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":"2306.03403","created_at":"2026-07-05T07:54:49.138958+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.03403v2","created_at":"2026-07-05T07:54:49.138958+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.03403","created_at":"2026-07-05T07:54:49.138958+00:00"},{"alias_kind":"pith_short_12","alias_value":"4EK4ZTV6N24G","created_at":"2026-07-05T07:54:49.138958+00:00"},{"alias_kind":"pith_short_16","alias_value":"4EK4ZTV6N24G3CEV","created_at":"2026-07-05T07:54:49.138958+00:00"},{"alias_kind":"pith_short_8","alias_value":"4EK4ZTV6","created_at":"2026-07-05T07:54:49.138958+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.27745","citing_title":"Panoramic Scene Understanding: A Survey from Distortion-Aware Engineering to Sphere-Native Modeling","ref_index":59,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4EK4ZTV6N24G3CEVLOK44DRLWR","json":"https://pith.science/pith/4EK4ZTV6N24G3CEVLOK44DRLWR.json","graph_json":"https://pith.science/api/pith-number/4EK4ZTV6N24G3CEVLOK44DRLWR/graph.json","events_json":"https://pith.science/api/pith-number/4EK4ZTV6N24G3CEVLOK44DRLWR/events.json","paper":"https://pith.science/paper/4EK4ZTV6"},"agent_actions":{"view_html":"https://pith.science/pith/4EK4ZTV6N24G3CEVLOK44DRLWR","download_json":"https://pith.science/pith/4EK4ZTV6N24G3CEVLOK44DRLWR.json","view_paper":"https://pith.science/paper/4EK4ZTV6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.03403&json=true","fetch_graph":"https://pith.science/api/pith-number/4EK4ZTV6N24G3CEVLOK44DRLWR/graph.json","fetch_events":"https://pith.science/api/pith-number/4EK4ZTV6N24G3CEVLOK44DRLWR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4EK4ZTV6N24G3CEVLOK44DRLWR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4EK4ZTV6N24G3CEVLOK44DRLWR/action/storage_attestation","attest_author":"https://pith.science/pith/4EK4ZTV6N24G3CEVLOK44DRLWR/action/author_attestation","sign_citation":"https://pith.science/pith/4EK4ZTV6N24G3CEVLOK44DRLWR/action/citation_signature","submit_replication":"https://pith.science/pith/4EK4ZTV6N24G3CEVLOK44DRLWR/action/replication_record"}},"created_at":"2026-07-05T07:54:49.138958+00:00","updated_at":"2026-07-05T07:54:49.138958+00:00"}