{"id":"W4390873233","doi":"10.1109/iccv51070.2023.00075","title":"MemorySeg: Online LiDAR Semantic Segmentation with a Latent Memory","year":2023,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Segmentation; Point cloud; Lidar; Artificial intelligence; Frame (networking); Representation (politics); Visibility; Semantics (computer science); Range (aeronautics); Point (geometry); Computer vision; Pattern recognition (psychology); Remote sensing","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":[0.00004974781,0.00009810461,0.00009234253,0.0001046302,0.0000349125,0.00002541598,0.00004306924,0.00003474771,0.0000832975],"category_scores_gemma":[0.000003953517,0.00007958835,0.00002105432,0.0003405765,0.0000106761,0.00006315271,0.000008328747,0.00005299876,0.0001651064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000309733,"about_ca_system_score_gemma":0.000008374926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001918742,"about_ca_topic_score_gemma":0.00006983415,"domain_scores_codex":[0.9994463,0.000009374708,0.0001290506,0.0001080847,0.0001498584,0.0001573268],"domain_scores_gemma":[0.9997699,0.00001710086,0.00001248566,0.0001226445,0.00003032572,0.00004755245],"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":[0.000004419775,0.0000157093,0.0004742113,0.00005889145,0.00002861746,0.00002116424,0.000160494,0.9889091,0.006624715,0.00008981922,0.001808343,0.001804565],"study_design_scores_gemma":[0.0003952195,0.00004585489,0.003831025,0.00002677198,0.00002234873,0.000004833225,0.0003070894,0.9839586,0.01104139,0.00003640337,0.0001705473,0.0001599002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8862576,0.00002389659,0.1093159,0.000274685,0.0002307429,0.0002335332,0.000007287475,0.00126221,0.002394187],"genre_scores_gemma":[0.99502,0.00005753631,0.002928196,0.00009841077,0.00006159671,0.000006943522,0.0002046,0.00004098063,0.001581682],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1087625,"threshold_uncertainty_score":0.3245519,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01502296277193868,"score_gpt":0.219523748402094,"score_spread":0.2045007856301553,"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."}}