{"id":"W2051470489","doi":"10.1109/tvcg.2015.2410272","title":"Regularization Based Iterative Point Match Weighting for Accurate Rigid Transformation Estimation","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Northern British Columbia","funders":"","keywords":"Computer science; Weighting; Transformation (genetics); Artificial intelligence; Regularization (linguistics); Iterative closest point; Rigid transformation; Matching (statistics); Point set registration; Point cloud; Pattern recognition (psychology); Algorithm; Computer vision; Point (geometry); 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":[0.002499964,0.001394407,0.001735629,0.002197645,0.000680868,0.001137931,0.002098524,0.001638504,0.001801091],"category_scores_gemma":[0.00818535,0.0008019797,0.001277128,0.001852877,0.000994539,0.00203728,0.002059333,0.001494486,0.001306045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007677693,"about_ca_system_score_gemma":0.001568739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003726355,"about_ca_topic_score_gemma":0.004209717,"domain_scores_codex":[0.9976005,0.0005144767,0.0001398041,0.0004496865,0.001118106,0.0001773691],"domain_scores_gemma":[0.9970714,0.001039604,0.0003798809,0.0005829586,0.0008465565,0.00007960413],"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":[0.0002664194,0.0001732289,0.001849572,0.0001761862,0.0001595546,0.0001133556,0.0002785258,0.3363307,0.05857074,0.009713521,0.002678446,0.5896898],"study_design_scores_gemma":[0.000008687259,0.0000409968,0.0003653741,0.00000691157,0.00001150743,0.00007582102,0.00001805113,0.9853043,0.01029767,0.002863466,0.0009874225,0.00001983452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005752864,0.00004932596,0.9935321,0.00001962457,0.000009852968,0.00001929145,0.000009398862,0.0004020988,0.0002054604],"genre_scores_gemma":[0.1543748,0.0001007959,0.8435206,0.00008311299,0.00002791923,0.0001157018,0.0001657048,0.0003766538,0.001234661],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003726355,"threshold_uncertainty_score":0.01322126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02407835219960853,"score_gpt":0.2608980186095815,"score_spread":0.2368196664099729,"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."}}