{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006767777,0.00160574,0.001098763,0.001372606,0.0003218128,0.001122891,0.002126571,0.001239735,0.006510667],"category_scores_gemma":[0.001216017,0.0007248738,0.001282743,0.001226279,0.0004988413,0.002002425,0.003367067,0.001643734,0.001886583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008535551,"about_ca_system_score_gemma":0.001014166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005981033,"about_ca_topic_score_gemma":0.007981128,"domain_scores_codex":[0.9994048,0.00006045046,0.00001819275,0.0001458088,0.0002932154,0.00007754003],"domain_scores_gemma":[0.9997452,0.0000454425,0.00002680793,0.00009371612,0.0000671724,0.00002159067],"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":[0.0004963275,0.0001930467,0.001287198,0.0002429024,0.0002908437,0.0001934199,0.0001248496,0.140926,0.03755065,0.01031014,0.0235815,0.7848031],"study_design_scores_gemma":[0.00003386012,0.00009536319,0.0006207387,0.00002774483,0.00003609254,0.0002844669,0.00003207451,0.9570496,0.02329673,0.01020314,0.00828761,0.00003257877],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0102062,0.0007792065,0.9750506,0.0001550948,0.0001042501,0.00008375693,0.001119699,0.01078846,0.001712768],"genre_scores_gemma":[0.2935947,0.001104496,0.6930426,0.0004106334,0.0001098307,0.0001703965,0.00577411,0.0009649096,0.004828329],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006510667,"threshold_uncertainty_score":0.02178037,"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."}}