{"id":"W2134019950","doi":"10.1109/tpami.2009.102","title":"Shape and Spatially-Varying BRDFs from Photometric Stereo","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":271,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Office of Naval Research; National Science Foundation","keywords":"Artificial intelligence; Computer science; Photometric stereo; Computer vision; Pixel; Specular reflection; Variety (cybernetics); Computer graphics (images); Image (mathematics); Pattern recognition (psychology); Optics; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001659893,0.0002124367,0.0002997165,0.001211726,0.0001934897,0.0003404119,0.0003965964,0.00006427507,0.00008701171],"category_scores_gemma":[0.000003383485,0.0001943006,0.000153497,0.002130769,0.00003493896,0.0002722244,0.000009492508,0.0001830923,0.000004131097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001462548,"about_ca_system_score_gemma":0.00001248226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001052347,"about_ca_topic_score_gemma":0.0003555079,"domain_scores_codex":[0.9985664,0.00006367227,0.0003536412,0.000579081,0.000252471,0.000184735],"domain_scores_gemma":[0.9991081,0.0001596057,0.0001045949,0.0004238284,0.00007004009,0.000133883],"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":[0.000004192873,0.0001166937,0.0009076435,0.00000483572,0.0001594971,0.000006365973,0.0003012461,0.0004305026,0.000254244,0.0004084848,0.000003305803,0.997403],"study_design_scores_gemma":[0.00009093148,0.0002195065,0.005534429,0.00002831567,0.0001751093,0.000005300014,0.000004842771,0.9109598,0.08065349,0.001990341,0.0000507322,0.0002872203],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0195858,0.0002440283,0.9795309,0.0002252137,0.0000876331,0.0001039973,0.00002239126,0.000165642,0.0000344199],"genre_scores_gemma":[0.9938882,0.001314663,0.003676987,0.001059592,0.0000176384,0.000006586881,0.000005521841,0.000006712753,0.00002408009],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9971158,"threshold_uncertainty_score":0.7923349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02506737751831176,"score_gpt":0.29243413542075,"score_spread":0.2673667579024382,"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."}}