{"id":"W2787460109","doi":"10.1109/tpami.2018.2799222","title":"A Benchmark Dataset and Evaluation for Non-Lambertian and Uncalibrated Photometric Stereo","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":167,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"National Research Foundation Singapore","keywords":"Photometric stereo; Artificial intelligence; Ground truth; Benchmark (surveying); Computer science; Computer vision; Stereopsis; Reflectivity; Remote sensing; Image (mathematics); Geology; Optics","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":[0.00311689,0.004235799,0.002219869,0.006200346,0.001659093,0.003020579,0.006005557,0.002851832,0.006702064],"category_scores_gemma":[0.00756138,0.0007498372,0.002825219,0.006342295,0.00113997,0.002402991,0.003369353,0.002389759,0.009864489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002676204,"about_ca_system_score_gemma":0.002291154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02433164,"about_ca_topic_score_gemma":0.03956724,"domain_scores_codex":[0.9950101,0.0006461715,0.0004451388,0.001194685,0.002286826,0.000417099],"domain_scores_gemma":[0.9956052,0.0005925289,0.0002938604,0.001483088,0.001710743,0.0003146162],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008173364,0.001266314,0.005491059,0.003321107,0.0005893359,0.0003824482,0.0001449268,0.0303416,0.01489107,0.004382614,0.7002597,0.2381125],"study_design_scores_gemma":[0.000931514,0.0010287,0.03158195,0.001312755,0.0004119629,0.004162813,0.0008599754,0.2938866,0.05840272,0.01159053,0.5953915,0.0004391876],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.08796769,0.01208884,0.1212143,0.001788445,0.002546142,0.003766168,0.6618853,0.0644396,0.04430354],"genre_scores_gemma":[0.03793853,0.001171609,0.09729636,0.0004637637,0.0001632148,0.0009757768,0.8554286,0.001581668,0.004980452],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02433164,"threshold_uncertainty_score":0.04838002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03600593234483072,"score_gpt":0.3372482476394871,"score_spread":0.3012423152946564,"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."}}