{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003877013,0.00007365989,0.00007816163,0.00001821477,0.00003578897,0.0000401186,0.00002735182,0.0000409663,0.0003869947],"category_scores_gemma":[0.00000619515,0.00007648081,0.00002421848,0.0001752482,0.00000541495,0.00005119592,0.000008397371,0.00004535569,0.0001125163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002396831,"about_ca_system_score_gemma":0.000009655939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007233246,"about_ca_topic_score_gemma":0.00004361953,"domain_scores_codex":[0.9995327,0.00001445296,0.0001264957,0.0000934206,0.00008832635,0.0001445793],"domain_scores_gemma":[0.9997792,0.00001733792,0.000009546598,0.0001210769,0.00003400713,0.00003881417],"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":[7.717662e-7,0.000007439311,0.0007576311,0.00002279948,0.00001605178,0.00001431506,0.00003873002,0.9894261,0.002801619,0.001018969,0.003827064,0.002068528],"study_design_scores_gemma":[0.0001784159,0.00000918783,0.001133862,0.00001963999,0.00001767799,0.00000579895,0.00005175026,0.9829206,0.01191068,0.0002094935,0.003405196,0.0001376828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05554601,0.0003764987,0.9251847,0.0001055131,0.0006632427,0.00009047083,0.000001418258,0.0003031627,0.01772904],"genre_scores_gemma":[0.97383,0.0001460901,0.02411615,0.0002691375,0.0002445201,0.000003507948,0.00009674669,0.00003209524,0.00126175],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.918284,"threshold_uncertainty_score":0.4237321,"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."}}