{"id":"W4412352804","doi":"10.1109/tro.2025.3588454","title":"End-to-End 2D-3D Registration Between Image and LiDAR Point Cloud for Vehicle Localization","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Robotics","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Point cloud; Lidar; Computer vision; Artificial intelligence; Computer science; Image registration; Cloud computing; End-to-end principle; Point (geometry); Remote sensing; Computer graphics (images); Image (mathematics); Geography; Mathematics; Geometry","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":[0.0003078073,0.001806944,0.001155162,0.001165182,0.0005548404,0.0008674043,0.002813102,0.0008061911,0.006491854],"category_scores_gemma":[0.001383478,0.0005790711,0.0007949061,0.001642688,0.0003713088,0.001741382,0.003102828,0.001346234,0.006514641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005538566,"about_ca_system_score_gemma":0.001105248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007384256,"about_ca_topic_score_gemma":0.0127724,"domain_scores_codex":[0.9993221,0.00004629928,0.00002356015,0.000196925,0.0003249737,0.00008620133],"domain_scores_gemma":[0.9996568,0.00002640483,0.00003041433,0.0001511382,0.0001120264,0.00002321346],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006662093,0.0004644703,0.004313386,0.0003015472,0.0001848177,0.0005232362,0.0001617912,0.09492405,0.04876994,0.00447292,0.04436538,0.8008523],"study_design_scores_gemma":[0.00006487665,0.0002149978,0.003606193,0.00002893541,0.00003403775,0.0004171572,0.0001259946,0.9248292,0.04525484,0.00484616,0.02052095,0.00005662658],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03504313,0.0003819267,0.9191086,0.0001622147,0.0001624407,0.0002299107,0.002242419,0.0376779,0.004991462],"genre_scores_gemma":[0.4248659,0.0005637509,0.5400046,0.000292243,0.00007706771,0.0005429713,0.0214543,0.001376799,0.01082224],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007384256,"threshold_uncertainty_score":0.02171737,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01924719162166084,"score_gpt":0.2795944582285054,"score_spread":0.2603472666068446,"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."}}