{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:SGGG3TQGU6A4PEEFSJ76LQNNZC","short_pith_number":"pith:SGGG3TQG","schema_version":"1.0","canonical_sha256":"918c6dce06a781c79085927fe5c1adc899b186b4f8c1047d152a6fdbc9d412da","source":{"kind":"arxiv","id":"2411.02179","version":3},"attestation_state":"computed","paper":{"title":"CleAR: Robust Context-Guided Generative Lighting Estimation for Mobile Augmented Reality","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR","cs.HC"],"primary_cat":"cs.CV","authors_text":"Mallesham Dasari, Tian Guo, Yiqin Zhao","submitted_at":"2024-11-04T15:37:18Z","abstract_excerpt":"High-quality environment lighting is essential for creating immersive mobile augmented reality (AR) experiences. However, achieving visually coherent estimation for mobile AR is challenging due to several key limitations in AR device sensing capabilities, including low camera FoV and limited pixel dynamic ranges. Recent advancements in generative AI, which can generate high-quality images from different types of prompts, including texts and images, present a potential solution for high-quality lighting estimation. Still, to effectively use generative image diffusion models, we must address two"},"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":"2411.02179","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-11-04T15:37:18Z","cross_cats_sorted":["cs.GR","cs.HC"],"title_canon_sha256":"eed4d8f49c9d6ba031ea9fba1bd80280c4311f6bd2b7d7699ece6438c09f8908","abstract_canon_sha256":"f646c2f474ea7763bfab62e34970fc8ad96aebfdf79e18571d6329afee5ece63"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:38:01.691608Z","signature_b64":"ZFCHz3joxzp8WzSuSQ3nU1orCSVsDemnfiji2ntFHZ2jxSR3HkGt77ZalB0Gh1llzQlftfLqmIOYq76Oab7/Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"918c6dce06a781c79085927fe5c1adc899b186b4f8c1047d152a6fdbc9d412da","last_reissued_at":"2026-07-05T11:38:01.691086Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:38:01.691086Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CleAR: Robust Context-Guided Generative Lighting Estimation for Mobile Augmented Reality","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GR","cs.HC"],"primary_cat":"cs.CV","authors_text":"Mallesham Dasari, Tian Guo, Yiqin Zhao","submitted_at":"2024-11-04T15:37:18Z","abstract_excerpt":"High-quality environment lighting is essential for creating immersive mobile augmented reality (AR) experiences. However, achieving visually coherent estimation for mobile AR is challenging due to several key limitations in AR device sensing capabilities, including low camera FoV and limited pixel dynamic ranges. Recent advancements in generative AI, which can generate high-quality images from different types of prompts, including texts and images, present a potential solution for high-quality lighting estimation. Still, to effectively use generative image diffusion models, we must address two"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.02179","kind":"arxiv","version":3},"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/2411.02179/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":"2411.02179","created_at":"2026-07-05T11:38:01.691143+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.02179v3","created_at":"2026-07-05T11:38:01.691143+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.02179","created_at":"2026-07-05T11:38:01.691143+00:00"},{"alias_kind":"pith_short_12","alias_value":"SGGG3TQGU6A4","created_at":"2026-07-05T11:38:01.691143+00:00"},{"alias_kind":"pith_short_16","alias_value":"SGGG3TQGU6A4PEEF","created_at":"2026-07-05T11:38:01.691143+00:00"},{"alias_kind":"pith_short_8","alias_value":"SGGG3TQG","created_at":"2026-07-05T11:38:01.691143+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.16714","citing_title":"TPIE: Topology-Preserved Image Editing With Text Instructions","ref_index":65,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SGGG3TQGU6A4PEEFSJ76LQNNZC","json":"https://pith.science/pith/SGGG3TQGU6A4PEEFSJ76LQNNZC.json","graph_json":"https://pith.science/api/pith-number/SGGG3TQGU6A4PEEFSJ76LQNNZC/graph.json","events_json":"https://pith.science/api/pith-number/SGGG3TQGU6A4PEEFSJ76LQNNZC/events.json","paper":"https://pith.science/paper/SGGG3TQG"},"agent_actions":{"view_html":"https://pith.science/pith/SGGG3TQGU6A4PEEFSJ76LQNNZC","download_json":"https://pith.science/pith/SGGG3TQGU6A4PEEFSJ76LQNNZC.json","view_paper":"https://pith.science/paper/SGGG3TQG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.02179&json=true","fetch_graph":"https://pith.science/api/pith-number/SGGG3TQGU6A4PEEFSJ76LQNNZC/graph.json","fetch_events":"https://pith.science/api/pith-number/SGGG3TQGU6A4PEEFSJ76LQNNZC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SGGG3TQGU6A4PEEFSJ76LQNNZC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SGGG3TQGU6A4PEEFSJ76LQNNZC/action/storage_attestation","attest_author":"https://pith.science/pith/SGGG3TQGU6A4PEEFSJ76LQNNZC/action/author_attestation","sign_citation":"https://pith.science/pith/SGGG3TQGU6A4PEEFSJ76LQNNZC/action/citation_signature","submit_replication":"https://pith.science/pith/SGGG3TQGU6A4PEEFSJ76LQNNZC/action/replication_record"}},"created_at":"2026-07-05T11:38:01.691143+00:00","updated_at":"2026-07-05T11:38:01.691143+00:00"}