{"id":"W4406708807","doi":"10.3390/rs17030357","title":"Enhancing Cross-Modal Camera Image and LiDAR Data Registration Using Feature-Based Matching","year":2025,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Image registration; Computer vision; Matching (statistics); Modal; Lidar; Remote sensing; Computer science; Feature (linguistics); Image matching; Image (mathematics); Geology; Materials science; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009341532,0.0009380619,0.0009825091,0.002112168,0.0004443365,0.001265594,0.001604124,0.0008713557,0.002563932],"category_scores_gemma":[0.003377168,0.0006405679,0.001105075,0.002558828,0.0005119316,0.002531337,0.002660508,0.001223759,0.002119411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006340799,"about_ca_system_score_gemma":0.001273392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005208145,"about_ca_topic_score_gemma":0.007190421,"domain_scores_codex":[0.9985139,0.000146514,0.0000757495,0.0004170896,0.000644368,0.0002025214],"domain_scores_gemma":[0.998848,0.0001298754,0.0001688473,0.0003935827,0.0004159814,0.00004367715],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002783906,0.0003225089,0.003757074,0.0001477827,0.000151955,0.0001948133,0.0003258618,0.07259109,0.09866154,0.004254319,0.004010195,0.8153044],"study_design_scores_gemma":[0.00002938404,0.0001637326,0.004391853,0.00002194026,0.00004049356,0.0003622431,0.0001642277,0.9174764,0.06666917,0.004503833,0.006118232,0.00005845041],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03286314,0.0001320474,0.9621865,0.00007781492,0.00005011351,0.00006848699,0.0001358576,0.003202816,0.00128328],"genre_scores_gemma":[0.3631223,0.000171237,0.6323909,0.0001886965,0.00003637372,0.0001002747,0.001053119,0.0004495315,0.002487565],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005208145,"threshold_uncertainty_score":0.01035565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01617853547232237,"score_gpt":0.277555119636993,"score_spread":0.2613765841646706,"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."}}