{"id":"W2166892589","doi":"10.1109/crv.2015.13","title":"Simultaneous Scene Reconstruction and Auto-Calibration Using Constrained Iterative Closest Point for 3D Depth Sensor Array","year":2015,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Iterative closest point; Computer vision; Computer science; Artificial intelligence; Overhead (engineering); Point cloud; Calibration; Robot; Planar; Point (geometry); Camera resectioning; Depth map; Field of view; Matching (statistics); Tracking (education); Computer graphics (images); Image (mathematics); Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.00009854007,0.000145611,0.0001579738,0.0000757953,0.00007111972,0.00009609322,0.00002274123,0.00009585005,0.00001251302],"category_scores_gemma":[0.0001194884,0.0001398455,0.00002584473,0.00009285354,0.00004966522,0.0002075778,0.000004224511,0.00005826354,0.000001771388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000079881,"about_ca_system_score_gemma":0.00003508388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001410944,"about_ca_topic_score_gemma":0.00002713193,"domain_scores_codex":[0.9993077,0.00002683724,0.0002406612,0.000171288,0.00009015443,0.0001633654],"domain_scores_gemma":[0.9995054,0.0001123515,0.00003983905,0.00008952535,0.0001425434,0.0001103607],"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":[0.00002825065,0.00001103015,0.0001710987,0.0000386303,0.00002726133,0.000003338422,0.0003566207,0.9558232,0.03157263,0.0006206855,0.00004312554,0.01130408],"study_design_scores_gemma":[0.0006690981,0.00007227284,0.000008444643,0.00002683504,0.00002325736,0.00007016768,0.0003063882,0.984444,0.01376364,0.0002778549,0.0001552124,0.0001827841],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08065938,0.00003522588,0.9174246,0.00006238943,0.000323129,0.0003304047,0.00001574581,0.0002044536,0.0009446208],"genre_scores_gemma":[0.7519829,0.000006427627,0.247739,0.00004962097,0.0001060803,0.000004016523,0.00004134792,0.00002537149,0.00004529815],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6713235,"threshold_uncertainty_score":0.5702732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0271455647121159,"score_gpt":0.2422480695867906,"score_spread":0.2151025048746747,"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."}}