{"id":"W3206593131","doi":"10.1109/icra48506.2021.9561305","title":"S3Net: 3D LiDAR Sparse Semantic Segmentation Network","year":2021,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Point cloud; Computer science; Artificial intelligence; Segmentation; Lidar; Convolutional neural network; Feature (linguistics); Computer vision; Projection (relational algebra); Convolution (computer science); Semantics (computer science); Pattern recognition (psychology); Artificial neural network; Remote sensing; Algorithm; Geography","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":[0.000200453,0.001653706,0.0007210383,0.0009382841,0.00043986,0.0006149869,0.002082702,0.001111401,0.004551976],"category_scores_gemma":[0.000682917,0.0005491224,0.0007136315,0.0009113055,0.0004927511,0.001536445,0.001141359,0.0008971167,0.001894364],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001170389,"about_ca_system_score_gemma":0.001341767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02475507,"about_ca_topic_score_gemma":0.03476811,"domain_scores_codex":[0.9998011,0.00001614272,0.000007487744,0.00008175319,0.00005200584,0.00004149467],"domain_scores_gemma":[0.999876,0.00002165923,0.00001714656,0.00003093073,0.0000406317,0.00001359452],"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":[0.0005969565,0.0003095857,0.002429062,0.0003542819,0.000197231,0.0003894586,0.0001407791,0.2629106,0.02744855,0.01054135,0.07296233,0.6217198],"study_design_scores_gemma":[0.00002346423,0.00006763916,0.0005796546,0.00001967781,0.00002491124,0.00008971876,0.00002807406,0.9765918,0.009791749,0.006245628,0.006518106,0.00001957928],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.129459,0.002216994,0.7799906,0.001074408,0.0005044519,0.0003854526,0.0119195,0.0586479,0.01580184],"genre_scores_gemma":[0.682847,0.0009892185,0.2684449,0.0009216129,0.0001153707,0.000440024,0.02918456,0.0007861952,0.0162712],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02475507,"threshold_uncertainty_score":0.04922193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01075254598406885,"score_gpt":0.2044037123001163,"score_spread":0.1936511663160474,"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."}}