{"id":"W2588613660","doi":"10.5194/isprsarchives-xl-1-w4-269-2015","title":"PLANAR CONSTRAINTS FOR AN IMPROVED UAV-IMAGE-BASED DENSE POINT CLOUD GENERATION","year":2015,"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":"Advanced Vision and Imaging","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Point cloud; Planar; Image (mathematics); Computer science; Outlier; Smoothness; Computer vision; Constraint (computer-aided design); Artificial intelligence; Point (geometry); Matching (statistics); Mathematics; Computer graphics (images); 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":[0.0004903082,0.000989996,0.001016348,0.001825557,0.0004785684,0.0006985409,0.00172445,0.0007401032,0.002474655],"category_scores_gemma":[0.001779945,0.0007380391,0.001145489,0.001891149,0.000361981,0.00111112,0.001816255,0.001146459,0.0009924967],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000555968,"about_ca_system_score_gemma":0.001209551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00799857,"about_ca_topic_score_gemma":0.00975131,"domain_scores_codex":[0.9991086,0.00008141384,0.00003764766,0.0001376997,0.0005577598,0.00007681638],"domain_scores_gemma":[0.9992332,0.0001579691,0.00009146438,0.0002076581,0.0002764792,0.00003322859],"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":[0.0001639743,0.0001150397,0.001825016,0.000223878,0.00009185457,0.0002682034,0.0002191748,0.287454,0.08702055,0.0102132,0.005002531,0.6074026],"study_design_scores_gemma":[0.00001386822,0.00002392437,0.0003844413,0.000007065201,0.00001001517,0.00008807736,0.00002141852,0.9862354,0.009734419,0.001729833,0.00173752,0.00001392778],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004553176,0.00004392548,0.9944198,0.00002236735,0.00001388378,0.00004130264,0.00005882264,0.0005187324,0.0003279693],"genre_scores_gemma":[0.1265858,0.0001092452,0.8714281,0.00005270312,0.00002018585,0.0001007192,0.0006597406,0.0001811514,0.0008623165],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00799857,"threshold_uncertainty_score":0.01590401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03339419469427209,"score_gpt":0.2864019603661653,"score_spread":0.2530077656718932,"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."}}