{"id":"W4214936898","doi":"10.1109/tits.2022.3154537","title":"CL3D: Camera-LiDAR 3D Object Detection With Point Feature Enhancement and Point-Guided Fusion","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Beijing Municipal Natural Science Foundation; China Postdoctoral Science Foundation; Beihang University; National Natural Science Foundation of China","keywords":"Lidar; Artificial intelligence; Computer vision; Computer science; Object detection; Feature (linguistics); Point cloud; Point (geometry); RGB color model; Pattern recognition (psychology); Remote sensing; Geography; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002818719,0.0003438352,0.000306575,0.0003279748,0.0009850324,0.000114759,0.0003804257,0.00007985817,0.00006810395],"category_scores_gemma":[0.000001114554,0.000327248,0.000108197,0.001016187,0.00005864198,0.0005008599,0.000002467843,0.0005391436,0.00002682061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002921808,"about_ca_system_score_gemma":0.00006490761,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001530016,"about_ca_topic_score_gemma":0.0002720536,"domain_scores_codex":[0.9973096,0.0001691564,0.0005910118,0.00083433,0.000732428,0.000363509],"domain_scores_gemma":[0.9987195,0.0001229505,0.0002115524,0.000617394,0.0001603399,0.0001682726],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003901766,0.0006515119,0.00006688383,0.0001513897,0.0002010769,0.00005023209,0.006200174,0.862254,0.04560394,0.002089826,0.0003631956,0.0819776],"study_design_scores_gemma":[0.003460007,0.004543359,0.0008882245,0.0003848484,0.0003656908,0.0008162202,0.005072838,0.2684529,0.6563824,0.0005209522,0.05641503,0.002697524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04073968,0.0001731634,0.9557375,0.0004205109,0.0010054,0.001406495,0.00006154204,0.0003457721,0.000109937],"genre_scores_gemma":[0.9913182,0.0001704553,0.005970989,0.0002300634,0.0000448263,0.001466585,0.00002947634,0.00004077494,0.0007286405],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9505785,"threshold_uncertainty_score":0.999918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01685132038772213,"score_gpt":0.2475263018957712,"score_spread":0.2306749815080491,"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."}}