{"id":"W2085067995","doi":"10.4028/www.scientific.net/kem.419-420.305","title":"Automatic Point Clouds Registration Based on the Method of Least Squares","year":2009,"lang":"en","type":"article","venue":"Key engineering materials","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Point cloud; Coordinate system; Surface (topology); Moving least squares; Point (geometry); Least-squares function approximation; Computer vision; Point set registration; Projection (relational algebra); Transformation (genetics); Matching (statistics); Algorithm; Artificial intelligence; Rigid transformation; Object (grammar); Surface reconstruction; Mathematics; Computer science; Iterative closest point; Geometry; Mathematical analysis","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007475889,0.00009944816,0.0001462703,0.00002510606,0.00004552788,0.00005637497,0.0001185258,0.00003823857,0.001296373],"category_scores_gemma":[0.0001250921,0.00006034544,0.0000320666,0.00008459968,0.000009842393,0.00005729193,0.000001495404,0.0000415717,0.00003657803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002819738,"about_ca_system_score_gemma":0.00001313824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001553666,"about_ca_topic_score_gemma":0.0000106135,"domain_scores_codex":[0.9993023,0.0001074013,0.0002021533,0.0001021363,0.000151169,0.0001348394],"domain_scores_gemma":[0.9995649,0.0001590853,0.00006289449,0.0001649045,0.00001655163,0.00003164453],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001302121,0.00006955001,0.001898063,0.0003620347,0.00004149704,0.00001951022,0.0009905593,0.4040602,0.5450113,0.004276094,0.001949153,0.04119181],"study_design_scores_gemma":[0.0003017717,0.0005272751,0.5797158,0.0003025447,0.00002279468,0.00001370608,0.0001025116,0.2620445,0.1556554,0.0003981947,0.0005509854,0.0003644602],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9944392,0.00003044291,0.002924797,0.0005539908,0.0003170445,0.0001345106,0.0000539278,0.0001121609,0.001433946],"genre_scores_gemma":[0.9947941,0.000001610507,0.004962708,0.00009981916,0.00006096855,9.81372e-7,0.00004047702,0.0000025262,0.00003686749],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5778178,"threshold_uncertainty_score":0.9996166,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01616620313764477,"score_gpt":0.2174552538294079,"score_spread":0.2012890506917631,"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."}}