{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":["sts"],"category_scores_codex":[0.0020357,0.0004237506,0.0004083957,0.0008488842,0.001311808,0.00131824,0.002231528,0.00008520169,0.000003027971],"category_scores_gemma":[0.001141305,0.0002760671,0.0003329478,0.0008013498,0.002878598,0.00120213,0.0006982944,0.0003606882,0.000002534102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006411687,"about_ca_system_score_gemma":0.0004782501,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.4269939,"about_ca_topic_score_gemma":0.08172362,"domain_scores_codex":[0.9955776,0.000334626,0.001390473,0.0005095749,0.001637175,0.0005505607],"domain_scores_gemma":[0.9960192,0.0008687004,0.001607457,0.0006348398,0.0006022976,0.0002675503],"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":[0.0001405982,0.00002564043,0.0000449437,0.0000231244,0.00004395753,2.389903e-7,0.002750081,0.003447999,0.00731311,0.00003365195,0.0001091056,0.9860675],"study_design_scores_gemma":[0.001321403,0.0002897608,0.0004032644,0.0001775219,0.0000269635,0.0001205404,0.001593966,0.969824,0.01256999,0.01060344,0.002736401,0.0003328138],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002863246,0.00001963111,0.9847887,0.005674608,0.002562685,0.0008471489,0.00009768785,0.00007488142,0.003071381],"genre_scores_gemma":[0.9395621,0.00003263741,0.05773481,0.002354248,0.0002110003,7.197118e-7,0.00005210124,0.00001146515,0.00004093183],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9857348,"threshold_uncertainty_score":0.9999884,"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."}}