{"id":"W3197158243","doi":"10.3390/s21175778","title":"TIF-Reg: Point Cloud Registration with Transform-Invariant Features in SE(3)","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Wuhan Institute of Technology","keywords":"Point cloud; Rigid transformation; Artificial intelligence; Embedding; Invariant (physics); Singular value decomposition; Computer science; Translation (biology); Image registration; Rotation (mathematics); Algorithm; Transformation (genetics); Feature extraction; Mean squared error; Computer vision; Pattern recognition (psychology); Mathematics; Image (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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008208216,0.0001076408,0.0001445633,0.00006123911,0.00002731484,0.00003627405,0.00004022485,0.00006396673,0.00003051616],"category_scores_gemma":[0.00001154083,0.00009344808,0.00005124816,0.0002481764,0.00001268715,0.00005010459,0.000002769988,0.0001603813,0.00001316764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003537031,"about_ca_system_score_gemma":0.00002147057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009414461,"about_ca_topic_score_gemma":0.001524405,"domain_scores_codex":[0.9993948,0.00002074411,0.0001448214,0.0001513486,0.0001236105,0.0001646331],"domain_scores_gemma":[0.999733,0.00001516459,0.00001222382,0.0001652248,0.00003269705,0.00004168626],"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":[0.00001401963,0.00002057553,0.0001125492,0.00005197293,0.00006612781,0.0002069938,0.001308793,0.9919446,0.003077815,0.000406839,0.0006982036,0.002091545],"study_design_scores_gemma":[0.001091295,0.00005392798,0.001850458,0.0002551476,0.0001262528,0.0001606318,0.002026828,0.942259,0.04842968,0.001037168,0.0020584,0.0006512018],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9690694,0.0004078877,0.01184867,0.001480323,0.0001123013,0.00007014966,0.000008703629,0.0002099512,0.01679265],"genre_scores_gemma":[0.9973436,0.00008405287,0.0009470419,0.00004282952,0.00007797258,0.000002740185,0.00002677678,0.00002077878,0.001454211],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04968556,"threshold_uncertainty_score":0.3810702,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008711257501875996,"score_gpt":0.2058507729264538,"score_spread":0.1971395154245779,"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."}}