{"id":"W2486866072","doi":"10.1109/jstars.2016.2583789","title":"A Study on In Situ Calibration of an Off-The-Shelf Digital Camera Integrated to a Lidar System","year":2016,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Optech (Canada)","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Lidar; Photogrammetry; Remote sensing; Calibration; Point cloud; Bundle adjustment; Camera resectioning; Ranging; Orientation (vector space); Computer vision; Computer science; Digital camera; Pixel; Artificial intelligence; Geology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008934865,0.0004276227,0.0003961858,0.0003948332,0.0003359774,0.0005746224,0.0006970367,0.0007368926,0.0007607362],"category_scores_gemma":[0.00313479,0.0002739018,0.0002614838,0.0005426425,0.0002711875,0.0006492554,0.0004123806,0.0003294387,0.000218017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003308595,"about_ca_system_score_gemma":0.0002949206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001117607,"about_ca_topic_score_gemma":0.001390323,"domain_scores_codex":[0.9987293,0.0002676543,0.00006229221,0.0003097164,0.0005335144,0.00009755563],"domain_scores_gemma":[0.9979554,0.0004436111,0.0002321714,0.0004039573,0.0008951275,0.000069641],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005859028,0.0004695557,0.02406682,0.000363374,0.00008365457,0.0004993803,0.0009818071,0.01134577,0.875173,0.0004811973,0.000378909,0.08557068],"study_design_scores_gemma":[0.00006992572,0.003788094,0.09431275,0.0000595209,0.0002277621,0.001065113,0.0008722492,0.07181734,0.8216291,0.0001814496,0.00591616,0.00006063127],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9371058,0.0002007605,0.06044204,0.00005126059,0.00005182617,0.00008753031,0.00005673137,0.0001833562,0.001820541],"genre_scores_gemma":[0.9813592,0.00009788568,0.01773926,0.00003024354,0.00001003926,0.00002445915,0.00005203267,0.0000294479,0.0006574696],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001117607,"threshold_uncertainty_score":0.004725277,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01875599640948832,"score_gpt":0.2332533109503155,"score_spread":0.2144973145408272,"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."}}