{"id":"W4383888593","doi":"10.20537/2076-7633-2023-15-3-657-674","title":"Analysis of mixed reality cross-device global localization algorithms based on point cloud registration","year":2023,"lang":"en","type":"article","venue":"Computer Research and Modeling","topic":"Augmented Reality Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Instituto de Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México; Canadian Institute for Advanced Research","keywords":"Point cloud; Augmented reality; Computer science; Point (geometry); Algorithm; Computer vision; Artificial intelligence; Mathematics; Geometry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002428045,0.0001027627,0.0001863042,0.0003687275,0.0004686017,0.0003405702,0.000527262,0.0000752931,7.456592e-7],"category_scores_gemma":[0.00007922541,0.0001021429,0.00007090252,0.003494342,0.0001199103,0.0002087297,0.0003190431,0.0001420131,0.000007002514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001230683,"about_ca_system_score_gemma":0.0001259302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003792221,"about_ca_topic_score_gemma":0.000128702,"domain_scores_codex":[0.997695,0.0002194156,0.0004010569,0.0005477583,0.0007716699,0.0003650765],"domain_scores_gemma":[0.9980696,0.000254745,0.0000882592,0.0007534567,0.0006701899,0.000163696],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001808932,0.00006334771,0.0004354804,0.0000244399,0.00005849685,0.00000260839,0.00005603191,0.9522623,0.00001793697,0.02439894,0.0004454192,0.02221694],"study_design_scores_gemma":[0.0002109205,0.00009851091,0.001343107,0.00002557967,0.00001191836,4.54729e-7,0.00001247916,0.9906963,0.00008238284,0.007302057,0.0001328327,0.00008348679],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0162945,0.00002509151,0.9818814,0.001139308,0.0000814637,0.0002207395,0.00002093099,0.0001513026,0.0001851949],"genre_scores_gemma":[0.9870605,0.00006425031,0.01252022,0.00005335009,0.00007350696,0.00003039699,0.0001673057,0.000006253707,0.0000241407],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9707661,"threshold_uncertainty_score":0.4165268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1885991131877622,"score_gpt":0.4371278702328782,"score_spread":0.2485287570451161,"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."}}