{"id":"W3174969937","doi":"10.1109/cvpr46437.2021.01573","title":"Mirror3D: Depth Refinement for Mirror Surfaces","year":2021,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Compute Canada","keywords":"Computer science; Context (archaeology); Artificial intelligence; Depth map; RGB color model; Plane (geometry); Key (lock); Computer vision; Surface (topology); Plane mirror; Depth perception; Optics; Mathematics; Geometry; Geology; Physics; Image (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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007664695,0.003528448,0.001562596,0.001696382,0.0005770648,0.001421958,0.003338085,0.001393413,0.007694372],"category_scores_gemma":[0.003554829,0.001011396,0.002080272,0.001236354,0.0005976813,0.001725152,0.002709663,0.002108268,0.006391193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008028899,"about_ca_system_score_gemma":0.001450591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01380674,"about_ca_topic_score_gemma":0.04357324,"domain_scores_codex":[0.9986745,0.0001071646,0.00005691045,0.0005063401,0.0005247356,0.0001303476],"domain_scores_gemma":[0.9991574,0.0001275058,0.00008083785,0.000397668,0.0001924646,0.00004407322],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001254786,0.0005240293,0.0251164,0.001952262,0.0009375578,0.0003289114,0.0003203718,0.07933148,0.05329784,0.004289018,0.3193925,0.5132548],"study_design_scores_gemma":[0.0004434559,0.0003824534,0.01977818,0.0002757893,0.0001740721,0.001099339,0.0002997241,0.7327412,0.0962497,0.008186836,0.1401814,0.0001877598],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1417837,0.004583484,0.3947875,0.0009607958,0.001034415,0.0009841397,0.2060293,0.2391184,0.01071818],"genre_scores_gemma":[0.2105532,0.0007697908,0.5056078,0.0004400635,0.0001065965,0.0005022521,0.2716493,0.005953182,0.00441784],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01380674,"threshold_uncertainty_score":0.02745271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04131132006777828,"score_gpt":0.3270345847215561,"score_spread":0.2857232646537778,"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."}}