{"id":"W4366674458","doi":"10.1109/iccicc57084.2022.10101652","title":"Semantic Segmentation of Large-Scale Point Clouds by Encoder-Decoder Shared MLPs with Weighted Focal Loss","year":2022,"lang":"en","type":"article","venue":"","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Point cloud; Block (permutation group theory); Segmentation; Encoder; Artificial intelligence; Encoding (memory); Pooling; Perceptron; Residual; Intersection (aeronautics); Computer vision; Pattern recognition (psychology); Algorithm; Artificial neural network; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001408579,0.0001314286,0.0002074981,0.00008162545,0.00009691189,0.00001705503,0.0001160756,0.00003103798,0.001999561],"category_scores_gemma":[0.000001363515,0.0001157752,0.0000730746,0.0002828755,0.00001348016,0.00008314072,0.00004280994,0.0001351839,0.00001261271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005981721,"about_ca_system_score_gemma":0.00001396716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005337383,"about_ca_topic_score_gemma":0.00006201905,"domain_scores_codex":[0.9990352,0.00003004468,0.0002476449,0.0001831254,0.0002898961,0.0002141049],"domain_scores_gemma":[0.9996746,0.0000189514,0.00003642301,0.0001793574,0.0000365116,0.00005415035],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005616975,0.0002925358,0.003561749,0.0001545299,0.0004549579,0.00001580757,0.002947021,0.9570162,0.01974941,0.00006238007,0.01401535,0.001673913],"study_design_scores_gemma":[0.0005695292,0.00006422998,0.00004964318,0.00001038297,0.00007349294,0.000005803025,0.001098424,0.9888406,0.008861987,0.00008241664,0.0001714407,0.0001720485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4309998,0.0001392694,0.567234,0.0001275094,0.00004763408,0.00007850095,0.00008725097,0.0001734033,0.001112663],"genre_scores_gemma":[0.993642,0.00001516084,0.005294779,0.00007881391,0.00001743805,0.00002989208,0.0002021892,0.00003283648,0.0006869438],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5626422,"threshold_uncertainty_score":0.9989128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005617070180356102,"score_gpt":0.2080669313249627,"score_spread":0.2024498611446066,"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."}}