{"id":"W3107529234","doi":"10.1109/cvpr46437.2021.01393","title":"DeRF: Decomposed Radiance Fields","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Simon Fraser University; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Radiance; Rendering (computer graphics); Computer science; Inference; Limiting; Deep neural networks; Artificial neural network; Voronoi diagram; Artificial intelligence; Decomposition; Computer vision; Global illumination; Algorithm; Computer graphics (images); Remote sensing; Mathematics; Geography; Engineering","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":[0.00009408609,0.0001865907,0.0002357836,0.00007260598,0.00007220107,0.0004551394,0.001265878,0.0001305553,0.0001819408],"category_scores_gemma":[0.00004326992,0.000178905,0.0001268668,0.0001527692,0.00002001002,0.000304331,0.002091527,0.0005086548,0.00006008254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000354168,"about_ca_system_score_gemma":0.0001740812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002801359,"about_ca_topic_score_gemma":0.00001622264,"domain_scores_codex":[0.9986307,0.00004327869,0.0002203757,0.0006578959,0.000197489,0.0002502682],"domain_scores_gemma":[0.9984977,0.00006701772,0.0000821155,0.001144793,0.00008958307,0.0001188348],"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":[0.00000224653,0.00009102092,0.000275459,0.00009933717,0.00004017735,0.0002225315,0.000857311,0.00363296,0.0004888283,0.01436358,0.01159517,0.9683314],"study_design_scores_gemma":[0.0003421004,0.0000174583,0.001084803,0.0002460603,0.000005672532,0.00003838442,0.00006025624,0.9472104,0.008090788,0.01592582,0.02624307,0.0007352053],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0007452298,0.0009244523,0.9762055,0.003175262,0.001478328,0.00009689111,5.100949e-7,0.0002885472,0.01708529],"genre_scores_gemma":[0.18814,0.0001822046,0.8049444,0.004738075,0.0000882634,0.00001072247,0.000006699209,0.00001051878,0.001879073],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9675962,"threshold_uncertainty_score":0.7295532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02044352253078601,"score_gpt":0.2969076180212772,"score_spread":0.2764640954904912,"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."}}