{"id":"W2970086547","doi":"","title":"Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer","year":2019,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":99,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; University of Toronto","funders":"","keywords":"Rendering (computer graphics); Computer science; Shader; Computer graphics (images); Differentiable function; Artificial intelligence; Computer vision; Vertex (graph theory); Graphics pipeline; Interpolation (computer graphics); Image-based modeling and rendering; Real-time rendering; Graphics; 3D computer graphics; Theoretical computer science; Animation; Mathematics","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":[0.00006887404,0.0001219884,0.000115056,0.0001693889,0.0001187849,0.00008012697,0.0004881058,0.00002878642,0.0001005447],"category_scores_gemma":[0.00001365723,0.0001158108,0.00003004123,0.0005585395,0.00002086347,0.0008931101,0.0001408329,0.0001520245,0.0001549041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000508731,"about_ca_system_score_gemma":0.00005386693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001995147,"about_ca_topic_score_gemma":0.00001215822,"domain_scores_codex":[0.9990412,0.00005885956,0.00007263861,0.0005133665,0.0000778785,0.000236062],"domain_scores_gemma":[0.9991869,0.00005161737,0.00005918491,0.0004697122,0.00007450672,0.0001581313],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002547706,0.0002129085,0.2671608,0.00003516042,0.00004362776,0.00009443733,0.001542124,0.6973615,0.003893438,0.02136151,0.0001183258,0.007921392],"study_design_scores_gemma":[0.0005756229,0.0003928434,0.008335046,0.00004738023,0.000005850459,0.000001374867,0.000232359,0.9891813,0.0002763649,0.0003376039,0.000434716,0.0001794901],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.36827,0.000001574804,0.6291153,0.00002450635,0.0000556644,0.00008807681,3.385213e-7,0.0001483172,0.002296241],"genre_scores_gemma":[0.9782967,9.489263e-7,0.01814905,0.0002313866,0.00001178741,2.489421e-7,0.000003155936,0.00001022521,0.003296473],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6109662,"threshold_uncertainty_score":0.4722629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02273478599348454,"score_gpt":0.1786754144829777,"score_spread":0.1559406284894931,"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."}}