{"id":"W4385696128","doi":"10.1109/lra.2023.3303696","title":"Multi-Modal Streaming 3D Object Detection","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lidar; Computer science; Artificial intelligence; Computer vision; Context (archaeology); Object detection; Perception; Field of view; Pattern recognition (psychology); Remote sensing; Geography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00008328979,0.00009451278,0.00007931436,0.0001200391,0.0002174417,0.0001046449,0.0001640143,0.00003221459,5.348293e-7],"category_scores_gemma":[0.000008837604,0.00009699897,0.00002455315,0.0005440946,0.00002650852,0.0003496637,0.00005230939,0.00008232846,0.00005473881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002928366,"about_ca_system_score_gemma":0.000006743707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004796502,"about_ca_topic_score_gemma":0.00000655557,"domain_scores_codex":[0.9992489,0.00002509287,0.0001510798,0.0002545122,0.0001311492,0.000189232],"domain_scores_gemma":[0.9995542,0.00008409453,0.00007517108,0.0002156407,0.00002212969,0.00004883513],"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":[6.67386e-7,0.00001322165,0.0001087825,0.00001164499,0.000008305265,0.000005991505,0.0002125908,0.7173938,0.1786899,0.0008150965,0.0002872172,0.1024528],"study_design_scores_gemma":[0.0001445275,0.00001000183,0.009545321,0.000008576822,0.00000347634,0.000007473876,0.000006105969,0.9865518,0.003236798,0.0001681537,0.0002057904,0.000111934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09266835,0.000007091899,0.9035448,0.002725478,0.0002560788,0.0001276762,9.908965e-7,0.0006598597,0.000009679329],"genre_scores_gemma":[0.8132926,0.00001769891,0.1859353,0.000599765,0.00008015234,0.00002374915,0.000004373444,0.00001015032,0.00003617326],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7206243,"threshold_uncertainty_score":0.3955503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01971774811059929,"score_gpt":0.2605686190998469,"score_spread":0.2408508709892476,"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."}}