{"id":"W3034616321","doi":"10.1109/cvpr42600.2020.00287","title":"Conv-MPN: Convolutional Message Passing Neural Network for Structured Outdoor Architecture Reconstruction","year":2020,"lang":"en","type":"article","venue":"","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Convolutional neural network; Graph; Embedding; Artificial intelligence; Pattern recognition (psychology); Feature extraction; Theoretical computer science; Computer vision","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":[0.0004699429,0.001241791,0.0006314119,0.0004952449,0.0002976728,0.0006184767,0.002202368,0.001074289,0.002832915],"category_scores_gemma":[0.001445191,0.0004934218,0.0008009639,0.0006728323,0.0006059876,0.001551038,0.0008937909,0.001562172,0.0009552444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008813143,"about_ca_system_score_gemma":0.0007749349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01099033,"about_ca_topic_score_gemma":0.01446557,"domain_scores_codex":[0.9998048,0.0000288761,0.000007647756,0.00006822836,0.00005725784,0.0000332845],"domain_scores_gemma":[0.9996791,0.00009763574,0.00003821553,0.00008223965,0.00008011072,0.00002267097],"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":[0.0001303232,0.00007231362,0.0008098822,0.0001131206,0.0001248855,0.0001241986,0.00005458351,0.6894426,0.01026915,0.00931044,0.008135046,0.2814135],"study_design_scores_gemma":[0.000003847963,0.00001802644,0.0001086316,0.000004742704,0.000007540495,0.00002413411,0.00000529317,0.993275,0.002400264,0.003278407,0.0008699172,0.000004104437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02257315,0.0004155493,0.9688419,0.0002604896,0.00009616873,0.00004811829,0.000444555,0.004694551,0.002625548],"genre_scores_gemma":[0.4841864,0.0004599625,0.5007436,0.0005073559,0.00008426263,0.0001472444,0.002761268,0.0005543416,0.01055552],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01099033,"threshold_uncertainty_score":0.02185267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01476964154547618,"score_gpt":0.2225706945979357,"score_spread":0.2078010530524595,"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."}}