{"id":"W4312098327","doi":"10.1117/12.2658785","title":"3D object classification from point clouds","year":2022,"lang":"en","type":"article","venue":"","topic":"Image Processing and 3D Reconstruction","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Point cloud; Deep learning; Artificial intelligence; Computer science; Segmentation; Convolutional neural network; Pattern recognition (psychology); Field (mathematics); Artificial neural network; Point (geometry); Image segmentation; Noise (video); Cognitive neuroscience of visual object recognition; Object (grammar); Computer vision; Image (mathematics); 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":[0.0006800191,0.002510988,0.002470038,0.008167814,0.0006884998,0.002989098,0.002219674,0.002670252,0.003728954],"category_scores_gemma":[0.001733648,0.001009074,0.003555797,0.006289157,0.0007080521,0.001842108,0.002525136,0.002024082,0.005055199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001151635,"about_ca_system_score_gemma":0.001094452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01114952,"about_ca_topic_score_gemma":0.01055345,"domain_scores_codex":[0.9986513,0.00006567521,0.00008046319,0.0003644337,0.0006132083,0.0002248916],"domain_scores_gemma":[0.9992409,0.0001266681,0.00007680874,0.0002242897,0.000273151,0.00005818012],"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":[0.000566458,0.0002647332,0.008054666,0.000553313,0.0002438067,0.000708369,0.0001359581,0.08605544,0.02477873,0.004320327,0.02816453,0.8461538],"study_design_scores_gemma":[0.00003376356,0.00009925346,0.007899928,0.0001737751,0.00007622113,0.0008044274,0.000258611,0.933005,0.03074069,0.00888536,0.01793955,0.00008335548],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07648578,0.004555162,0.8726819,0.0008101471,0.0006345844,0.0008632475,0.01560784,0.02243674,0.005924453],"genre_scores_gemma":[0.3904572,0.004657024,0.539941,0.000393492,0.0003281465,0.000592671,0.05752517,0.0009237626,0.005181589],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01114952,"threshold_uncertainty_score":0.02216923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01701759471680563,"score_gpt":0.2304822584506961,"score_spread":0.2134646637338904,"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."}}