{"id":"W2553256049","doi":"10.22260/isarc2016/0045","title":"Arbitrary 3D Object Extraction from Cluttered Laser Scans Using Local Features","year":2016,"lang":"en","type":"article","venue":"Proceedings of the ... ISARC","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"RANSAC; Computer science; Point cloud; Artificial intelligence; Computer vision; Hough transform; Feature extraction; Laser scanning; Object detection; Object (grammar); Set (abstract data type); Pattern recognition (psychology); Image (mathematics); Laser","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004329822,0.0007560988,0.0007726711,0.002827865,0.0004154555,0.001137836,0.0007937468,0.0005731409,0.001376914],"category_scores_gemma":[0.00107825,0.0004350728,0.0009516621,0.002148178,0.0005292005,0.001221336,0.001309656,0.0004601866,0.00122473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004270328,"about_ca_system_score_gemma":0.0007486216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002274552,"about_ca_topic_score_gemma":0.003955341,"domain_scores_codex":[0.9993215,0.00006410293,0.00003180854,0.0001591811,0.0003482995,0.00007511349],"domain_scores_gemma":[0.9994534,0.0001203909,0.00009070933,0.0001796425,0.0001356608,0.00002020254],"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":[0.0001400799,0.0000738707,0.004525064,0.0002385226,0.0001017499,0.0002784899,0.0003050883,0.03639935,0.2239876,0.002596437,0.001866649,0.7294871],"study_design_scores_gemma":[0.00002430902,0.0002996408,0.03053644,0.00006173967,0.0001102513,0.001604352,0.0004809375,0.6353225,0.3087873,0.00747903,0.01512624,0.000167388],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03514862,0.0001242153,0.9618677,0.00002524343,0.00001116099,0.00005990939,0.0001854398,0.001699757,0.0008779265],"genre_scores_gemma":[0.2615037,0.0002343513,0.7357145,0.00002870767,0.00001697334,0.0000893789,0.0009613197,0.0003007031,0.001150373],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002827865,"threshold_uncertainty_score":0.004606307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01926653707248822,"score_gpt":0.2212007852675668,"score_spread":0.2019342481950786,"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."}}