{"id":"W2147600846","doi":"10.1145/343002.343006","title":"Shadow volume reconstruction from depth maps","year":2000,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"University of Waterloo","keywords":"Rendering (computer graphics); Computer science; Computer graphics (images); Computer vision; Shadow mapping; Shadow (psychology); Artificial intelligence; Volume rendering; Polygon (computer graphics); Texture mapping; Volume (thermodynamics); Graphics; Computer graphics; Frame (networking)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001449871,0.0002052847,0.0001745236,0.0003976885,0.0003506704,0.00022717,0.001059327,0.0001586951,0.000387057],"category_scores_gemma":[0.000006127133,0.0002178558,0.0001882877,0.001211794,0.00008329094,0.0005774273,0.00001239223,0.0003165596,0.00008899373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002520987,"about_ca_system_score_gemma":0.00003945811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002223378,"about_ca_topic_score_gemma":0.0001591205,"domain_scores_codex":[0.9985207,0.00008422717,0.0003131454,0.0005365305,0.0002992662,0.0002460979],"domain_scores_gemma":[0.9984096,0.00009140294,0.00006518191,0.00121543,0.00009611301,0.0001222363],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001266265,0.0001957943,0.0007849781,0.000005298897,0.00005609808,0.000005235554,0.0002581965,0.0000773811,0.00003504219,0.07539267,0.001044298,0.9221324],"study_design_scores_gemma":[0.0009673899,0.0005346979,0.01462744,0.0001395864,0.00006072008,0.00009034872,0.00003012944,0.2353379,0.005023251,0.6792122,0.06285713,0.001119253],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01086781,0.0000537241,0.98586,0.0008299929,0.0004450429,0.0001634897,0.00003318621,0.0009178537,0.0008288744],"genre_scores_gemma":[0.9176331,0.001157533,0.07898376,0.001612696,0.00008582539,0.0000559347,0.00002790716,0.00003047278,0.0004128274],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9210131,"threshold_uncertainty_score":0.8883904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01994263863516633,"score_gpt":0.257114779095318,"score_spread":0.2371721404601517,"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."}}