{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001539822,0.00005008157,0.00005303945,0.00004699464,0.0002962642,0.0001113149,0.0003399369,0.00001427452,0.0002733368],"category_scores_gemma":[0.000009303665,0.00004683286,0.00002539423,0.0002245449,0.00001719237,0.0002655127,0.0001596207,0.0001129229,0.00005650615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004724797,"about_ca_system_score_gemma":0.00009485031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008571009,"about_ca_topic_score_gemma":0.000003016795,"domain_scores_codex":[0.9993408,0.00005761797,0.0001073433,0.0002310225,0.0001636603,0.00009960139],"domain_scores_gemma":[0.9996171,0.00002459681,0.00005230722,0.0002542152,0.00002646186,0.0000253368],"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":[0.00000567678,0.00006055718,0.0008424668,0.000003519724,0.00001078175,0.00000417816,0.0009718663,0.0001044749,0.008578428,0.02231474,0.005239136,0.9618642],"study_design_scores_gemma":[0.0005035396,0.0001007745,0.007615293,0.000006139166,0.00000894956,0.0001223708,0.0008548659,0.9116937,0.01108499,0.04006549,0.02758208,0.0003618275],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02516796,0.00008978481,0.9449281,0.001643492,0.001238903,0.00004987923,0.000002292134,0.0003484552,0.02653114],"genre_scores_gemma":[0.8868446,0.00000209343,0.1114439,0.0003743328,0.00006664908,0.00001746166,0.000006633011,0.000003596822,0.001240814],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9615024,"threshold_uncertainty_score":0.2992846,"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."}}