{"id":"W3081716097","doi":"10.1145/882262.882316","title":"Accurate light source acquisition and rendering","year":2003,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Rendering (computer graphics); Computer science; Global illumination; Graphics pipeline; Image-based lighting; Computer graphics (images); Computer vision; Light source; Image-based modeling and rendering; Real-time rendering; Artificial intelligence; Computer graphics; Optics; 3D computer graphics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009625156,0.001074144,0.001253729,0.0008305351,0.0005754637,0.003076234,0.001247346,0.001127701,0.008912765],"category_scores_gemma":[0.003385202,0.0009937249,0.0009168259,0.0006830547,0.0007515681,0.00261375,0.002417428,0.00223268,0.004340527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000822295,"about_ca_system_score_gemma":0.001195026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001375998,"about_ca_topic_score_gemma":0.001527951,"domain_scores_codex":[0.9989645,0.0001110564,0.00004493865,0.000151434,0.0006222455,0.0001057883],"domain_scores_gemma":[0.9989784,0.0002550866,0.00006044369,0.0004491849,0.0001968947,0.00005997611],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002795365,0.000135428,0.0008372278,0.0003491505,0.0000568175,0.0002483571,0.0003740518,0.1652388,0.2718689,0.08995051,0.01467095,0.4559902],"study_design_scores_gemma":[0.00006624392,0.00008699398,0.0007751611,0.0000412671,0.0000233621,0.0004467669,0.00009395184,0.7537468,0.1440863,0.05200468,0.04854371,0.0000847773],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001978695,0.0000558748,0.994511,0.00005661868,0.00002653576,0.00004481919,0.00006909105,0.001348793,0.001908481],"genre_scores_gemma":[0.1090208,0.0003885285,0.8846225,0.00008960766,0.00005477376,0.0001687403,0.0003287681,0.001126449,0.004199805],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008912765,"threshold_uncertainty_score":0.02981621,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01836151989655706,"score_gpt":0.2617882896165461,"score_spread":0.243426769719989,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}