{"id":"W2003671525","doi":"10.5194/isprsarchives-xxxviii-5-w12-61-2011","title":"ANALYSIS OF TWO TRIANGLE-BASED MULTI-SURFACE REGISTRATION ALGORITHMS OF IRREGULAR POINT CLOUDS","year":2012,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; University of Toronto","funders":"","keywords":"Iterative closest point; Point cloud; Delaunay triangulation; Point (geometry); Algorithm; Transformation (genetics); Surface (topology); Mathematics; Pairwise comparison; Iterative method; Point set registration; Rigid transformation; Computer science; Geometry; Computer vision; Artificial intelligence","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","sts"],"consensus_categories":[],"category_scores_codex":[0.002370335,0.0003839704,0.0005799495,0.0007879427,0.000833363,0.0002630516,0.001376926,0.00009456887,0.00002039747],"category_scores_gemma":[0.000588873,0.0002533196,0.0005869242,0.001951554,0.003917999,0.0004919004,0.0006541526,0.000327584,0.000003191505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006556767,"about_ca_system_score_gemma":0.0001560572,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7900499,"about_ca_topic_score_gemma":0.1404043,"domain_scores_codex":[0.9949757,0.000401618,0.001773247,0.0003724134,0.0019846,0.0004923662],"domain_scores_gemma":[0.9953756,0.001003405,0.002587416,0.0006418217,0.0002163006,0.0001754302],"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":[0.0001256899,0.00005674829,0.001785725,0.00002693134,0.000193407,8.747912e-8,0.003213517,0.0342154,0.009380815,0.000009139118,0.00002656436,0.950966],"study_design_scores_gemma":[0.0008450271,0.0001010441,0.01688491,0.0001583904,0.0001981064,0.00003239239,0.00128883,0.9551654,0.02271355,0.001108442,0.001246161,0.0002577082],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05285431,0.00002811684,0.9379572,0.001834132,0.0008502641,0.0006099094,0.0001284242,0.00003030716,0.005707346],"genre_scores_gemma":[0.9837011,0.00005404476,0.01559459,0.0004352288,0.00007114335,2.217463e-7,0.00006003657,0.00001075394,0.00007295438],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9507083,"threshold_uncertainty_score":0.9999919,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02228243594818577,"score_gpt":0.2743140050714847,"score_spread":0.2520315691232989,"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."}}