{"id":"W3204212368","doi":"10.1109/iccv48922.2021.01578","title":"VolumeFusion: Deep Depth Fusion for 3D Scene Reconstruction","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/CVF International Conference on Computer Vision (ICCV)","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Artificial intelligence; Computer science; Deep learning; Interpretability; Depth map; Computation; Computer vision; Kernel (algebra); Convolutional neural network; Pattern recognition (psychology); Convolution (computer science); Artificial neural network; Algorithm; Mathematics; Image (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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000332926,0.0003900676,0.0003873538,0.0003371984,0.0003875861,0.0007947887,0.001230946,0.0001515289,0.001093355],"category_scores_gemma":[0.000094746,0.0003836858,0.0002549409,0.0004679161,0.00006791134,0.001157594,0.0006269461,0.0003719265,0.000330742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002262546,"about_ca_system_score_gemma":0.0002443238,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001044843,"about_ca_topic_score_gemma":0.0000341632,"domain_scores_codex":[0.9965512,0.0001486504,0.0006827593,0.001292772,0.0008594324,0.0004652035],"domain_scores_gemma":[0.9968513,0.0002192999,0.0003113771,0.0008176154,0.001556229,0.0002441792],"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":[0.00005687806,0.000185105,0.00008149625,0.00001349567,0.00003365352,0.00005771329,0.0001533757,0.0004278316,0.008028922,0.01757234,0.003854177,0.969535],"study_design_scores_gemma":[0.001179204,0.000277473,0.0005781099,0.000379188,0.000008088894,0.0001942087,0.0000498528,0.9542412,0.006243418,0.00477363,0.03162475,0.0004508756],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002611656,0.00008673305,0.9755163,0.00579119,0.009555069,0.0002918831,0.00001561132,0.0001879392,0.005943589],"genre_scores_gemma":[0.1402638,0.0003693591,0.8521229,0.002886253,0.001361489,0.00005158482,0.000114087,0.00004106831,0.002789417],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9690841,"threshold_uncertainty_score":0.9998615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03610421610239099,"score_gpt":0.3199385939091591,"score_spread":0.2838343778067681,"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."}}