{"id":"W2947724293","doi":"10.5194/isprs-annals-iv-2-w5-365-2019","title":"PIECEWISE-PLANAR APPROXIMATION OF LARGE 3D DATA AS GRAPH-STRUCTURED OPTIMIZATION","year":2019,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Piecewise; Planar; Polygon mesh; Computation; Point cloud; Regular polygon; Graph; Computer science; Segmentation; Mathematical optimization; Set (abstract data type); Algorithm; Mathematics; Theoretical computer science; Artificial intelligence; 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":[0.0006999733,0.001023369,0.001017394,0.001154297,0.0003579019,0.001120152,0.001773884,0.001116242,0.002329493],"category_scores_gemma":[0.002932161,0.0007851884,0.001663921,0.00187762,0.0007765882,0.00149726,0.001603346,0.001982669,0.001068786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007987326,"about_ca_system_score_gemma":0.0008726735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004786348,"about_ca_topic_score_gemma":0.004639709,"domain_scores_codex":[0.9994588,0.0001531468,0.00002761946,0.0001076479,0.0002184966,0.00003420088],"domain_scores_gemma":[0.9987971,0.000674659,0.0001084071,0.0002151835,0.0001605299,0.00004420332],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002785423,0.00001882237,0.0002909848,0.00009913806,0.00004044435,0.00005843252,0.00005077146,0.9349192,0.003428256,0.01148952,0.001900746,0.04767583],"study_design_scores_gemma":[0.000001532549,0.000003913127,0.00002732018,0.000002869534,0.000001952209,0.00001233239,0.000003680579,0.9954388,0.0003263709,0.003497345,0.0006815048,0.000002488654],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001751231,0.00004860948,0.9974627,0.00004850579,0.00001048601,0.00001331891,0.00007527805,0.0002852821,0.0003045446],"genre_scores_gemma":[0.09053435,0.0002880348,0.9061595,0.00008774827,0.00004776882,0.0001402946,0.0007163127,0.0004038257,0.001622214],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004786348,"threshold_uncertainty_score":0.009516954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02806287860164066,"score_gpt":0.2896936270595283,"score_spread":0.2616307484578877,"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."}}