{"id":"W2204475758","doi":"10.5194/isprsannals-ii-3-w5-417-2015","title":"EDGE BASED 3D INDOOR CORRIDOR MODELING USING A SINGLE IMAGE","year":2015,"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":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; York University; Government of Ontario","keywords":"Vanishing point; RANSAC; Line segment; Computer science; Computer vision; Artificial intelligence; Line (geometry); Image (mathematics); Enhanced Data Rates for GSM Evolution; Landmark; Mathematics; Geometry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002576397,0.001001719,0.0006241992,0.001277656,0.000274258,0.001309904,0.001027175,0.0008141393,0.003082847],"category_scores_gemma":[0.0005759037,0.0005958913,0.001214645,0.0009702977,0.0004283473,0.0007209761,0.0009272255,0.0007343187,0.001219788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003917502,"about_ca_system_score_gemma":0.000964663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007994557,"about_ca_topic_score_gemma":0.0113802,"domain_scores_codex":[0.9997082,0.0000317521,0.00001030612,0.00007910332,0.0001272877,0.00004340205],"domain_scores_gemma":[0.9997448,0.00004127318,0.00004301489,0.00007523574,0.00006805208,0.00002755627],"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":[0.0004915142,0.0001650142,0.004187336,0.0003247864,0.0001653488,0.0006170968,0.0002871075,0.6372388,0.07334712,0.003630849,0.002583682,0.2769614],"study_design_scores_gemma":[0.000008322287,0.00004087526,0.00106429,0.00001574332,0.00001725647,0.0001514686,0.00004758683,0.9887379,0.008324618,0.0004702182,0.001099292,0.00002244015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06516114,0.0001747851,0.9282807,0.00007251476,0.00002964974,0.00009510193,0.0005367718,0.00313811,0.002511211],"genre_scores_gemma":[0.5004749,0.0003842633,0.4946273,0.00004962042,0.00002020734,0.00008977012,0.001280168,0.0003285791,0.0027451],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007994557,"threshold_uncertainty_score":0.01589608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09330848678473354,"score_gpt":0.3082748564068967,"score_spread":0.2149663696221631,"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."}}