{"id":"W2470496469","doi":"10.5194/isprs-archives-xli-b1-725-2016","title":"A FAST METHOD FOR MEASURING THE SIMILARITY BETWEEN 3D MODEL AND 3D POINT CLOUD","year":2016,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Point cloud; Similarity (geometry); Measure (data warehouse); Computer science; Point (geometry); Similarity measure; Object (grammar); Algorithm; Data mining; Cloud computing; Image (mathematics); Artificial intelligence; Mathematics; Pattern recognition (psychology); 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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.002124743,0.0004027016,0.0004447325,0.0006494459,0.001341512,0.0006288901,0.001317413,0.00009134066,0.000003255486],"category_scores_gemma":[0.0006850991,0.0002113385,0.000366702,0.0005814033,0.001898266,0.0004549791,0.0006612145,0.0003515558,0.000001240932],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004794804,"about_ca_system_score_gemma":0.0001301071,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.3566593,"about_ca_topic_score_gemma":0.0748071,"domain_scores_codex":[0.9963129,0.000221785,0.001253423,0.000358339,0.001352402,0.0005011866],"domain_scores_gemma":[0.9966136,0.001662574,0.0008674151,0.0004346034,0.0002758554,0.0001459444],"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":[0.00006397517,0.000007052927,0.0001235734,0.00004225869,0.0001351538,7.368443e-8,0.002617443,0.04821104,0.00195872,0.000006648362,0.00003318261,0.9468009],"study_design_scores_gemma":[0.0006524078,0.00006752913,0.0007778491,0.0003236603,0.00008975269,0.00004653627,0.001007764,0.9827582,0.004453827,0.008566542,0.0009696804,0.0002862383],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008334744,0.00003722714,0.9825447,0.004593825,0.0008530147,0.0005473772,0.000188163,0.00006587178,0.002835053],"genre_scores_gemma":[0.9821926,0.0001886038,0.01675018,0.0005891916,0.0001876994,6.99459e-7,0.00001831447,0.00001508425,0.00005758151],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9738579,"threshold_uncertainty_score":0.9999586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02472701456463323,"score_gpt":0.2621596730035151,"score_spread":0.2374326584388818,"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."}}