{"id":"W4404527184","doi":"10.1145/3687943","title":"Efficient Image-Space Shape Splatting for Monte Carlo Rendering","year":2024,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computer graphics (images); Rendering (computer graphics); Monte Carlo method; Computer vision; Space (punctuation); Image-based modeling and rendering; Artificial intelligence; Mathematics","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":[0.00037762,0.0002087929,0.0001587343,0.0006556463,0.0003831625,0.0005209869,0.0007881505,0.00009979211,0.000006492841],"category_scores_gemma":[0.00002181442,0.0002099965,0.0002716971,0.001393439,0.00005082483,0.0002183978,0.00003227534,0.0002838538,0.000004933468],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003580019,"about_ca_system_score_gemma":0.00005580152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001708523,"about_ca_topic_score_gemma":0.00001237296,"domain_scores_codex":[0.9985217,0.00003386123,0.0002757764,0.0005769783,0.0002832855,0.0003083536],"domain_scores_gemma":[0.9986079,0.0003453119,0.00004553334,0.0007670034,0.0001390797,0.00009516977],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001400623,0.0002280538,0.0000168505,0.0003028057,0.000156028,0.00002357932,0.002017411,0.005510008,0.001015883,0.8877438,0.001010663,0.101961],"study_design_scores_gemma":[0.0001251143,0.0001235649,0.00004977099,0.0001395563,0.00002151085,0.00001432698,0.00002178983,0.9804881,0.003170088,0.01017351,0.00543005,0.0002425679],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005391505,0.0002715318,0.9903967,0.001431368,0.0007318271,0.0003163636,0.00001891521,0.00138233,0.00005939213],"genre_scores_gemma":[0.8882751,0.0001515127,0.1110156,0.0002862919,0.0000568637,0.0001120207,0.000001963658,0.0000362566,0.00006445224],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9749781,"threshold_uncertainty_score":0.856341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02961980787615816,"score_gpt":0.3050776316852657,"score_spread":0.2754578238091076,"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."}}