{"id":"W4285308978","doi":"10.1109/tgrs.2022.3175758","title":"A Novel Approach to the Extraction of Key Points From 3-D Rigid Point Cloud Using 2-D Images Transformation","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"National Natural Science Foundation of China; Natural Science Foundation of Shanghai","keywords":"Point cloud; Transformation (genetics); Key (lock); Computer science; Transformation matrix; Rigid transformation; Geometric transformation; Artificial intelligence; Point (geometry); Matrix (chemical analysis); Image (mathematics); Cloud computing; Computer vision; Algorithm; Mathematics; Geometry","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":[0.0002896053,0.0009224099,0.0009465047,0.002549425,0.000506348,0.0008574324,0.001229361,0.0007933895,0.001561239],"category_scores_gemma":[0.0009963675,0.0006179888,0.001005987,0.002597542,0.0004786135,0.001681987,0.001650759,0.0009624152,0.001593584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003753636,"about_ca_system_score_gemma":0.000928779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002346085,"about_ca_topic_score_gemma":0.002175319,"domain_scores_codex":[0.9993328,0.00004738636,0.00004135446,0.0001466077,0.0003787839,0.00005292247],"domain_scores_gemma":[0.9996147,0.00006036993,0.00005306534,0.00009235107,0.0001577939,0.00002170925],"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":[0.0001115714,0.00009096548,0.0009977535,0.0003433823,0.00009064246,0.0004322625,0.0001779683,0.0213763,0.149303,0.01236936,0.005561104,0.8091456],"study_design_scores_gemma":[0.0000709294,0.0002550953,0.003347099,0.00005297306,0.00009054714,0.002523895,0.0001750234,0.8235674,0.1187361,0.01025918,0.04079633,0.0001254088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001283252,0.0001059834,0.9977483,0.0000278955,0.0000380681,0.0000387157,0.00003123329,0.0004292454,0.000297257],"genre_scores_gemma":[0.05155991,0.0004709549,0.9455497,0.00007632025,0.00005906071,0.0001423041,0.0002967631,0.0000907924,0.001754112],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002549425,"threshold_uncertainty_score":0.005222797,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01883454433123328,"score_gpt":0.226683123301984,"score_spread":0.2078485789707507,"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."}}