{"id":"W2779637370","doi":"10.1109/iccv.2017.241","title":"Raster-to-Vector: Revisiting Floorplan Transformation","year":2017,"lang":"en","type":"article","venue":"","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":230,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Nvidia; National Science Foundation","keywords":"Floorplan; Raster graphics; Computer science; Representation (politics); Heuristics; Vector graphics; Artificial intelligence; Transformation (genetics); Graphics; Set (abstract data type); Computer vision; Visualization; Pattern recognition (psychology); Computer graphics (images)","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002559221,0.00006515708,0.0000777129,0.00001489789,0.0005113787,0.0002511823,0.0001929265,0.00002602609,0.002266316],"category_scores_gemma":[0.00003407839,0.00004501726,0.00002858109,0.00002373791,0.00001441248,0.000394601,0.000003676115,0.00005207156,0.001507438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001326829,"about_ca_system_score_gemma":0.000005300903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001334243,"about_ca_topic_score_gemma":0.002481438,"domain_scores_codex":[0.9994884,0.00002672693,0.0001053867,0.0001068431,0.000111491,0.0001611846],"domain_scores_gemma":[0.9996635,0.00001922718,0.00003381665,0.000172675,0.00001604397,0.00009474958],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00001869887,0.000002119381,0.4283197,0.00002241991,0.00000632736,0.000003818643,0.0008432855,0.0001135061,0.0002163458,0.00009129829,0.0007472251,0.5696153],"study_design_scores_gemma":[0.0001003074,0.00003389408,0.9884636,0.00002324027,0.000002614532,0.000005953555,0.0002185804,0.001853808,0.0002173431,0.00003727155,0.008931719,0.0001117037],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7702808,0.00003741399,0.0003550815,0.0009170428,0.0002079628,0.00008417129,0.00002076357,0.00005655433,0.2280402],"genre_scores_gemma":[0.9977009,0.000006109935,0.0005054512,0.0002111764,0.0001349413,2.863904e-7,0.00004072058,0.000001177467,0.001399211],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5695036,"threshold_uncertainty_score":0.99927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03251219355801468,"score_gpt":0.2494446745164595,"score_spread":0.2169324809584448,"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."}}