{"id":"W3094186564","doi":"10.1109/bigmm50055.2020.00047","title":"LayART: Generating indoor layout using ARCore Transformations","year":2020,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Science and Engineering Research Board; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computer vision; RGB color model; Artificial intelligence; Floor plan; Computer graphics (images); Plan (archaeology); Camera phone; Mobile phone; Mobile device; Image (mathematics); Engineering drawing; Engineering","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":[0.000279294,0.001791029,0.000748064,0.001237357,0.0004796965,0.0008727229,0.001199291,0.0006267937,0.01107703],"category_scores_gemma":[0.0007545135,0.0007301494,0.001382988,0.0009955373,0.0004568549,0.0008024956,0.001512099,0.000924276,0.00361573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004035252,"about_ca_system_score_gemma":0.0007417009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003299754,"about_ca_topic_score_gemma":0.008183547,"domain_scores_codex":[0.9995396,0.00004385657,0.00001528554,0.0001695347,0.000184912,0.00004682853],"domain_scores_gemma":[0.9996885,0.0000575555,0.00002598602,0.0001246093,0.00007039725,0.00003288377],"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":[0.0002850055,0.000214021,0.001488965,0.0004812551,0.0001385715,0.0005276591,0.0004632838,0.2156157,0.07256109,0.008770892,0.01933972,0.6801138],"study_design_scores_gemma":[0.00008000958,0.0001713671,0.001084781,0.00003750256,0.00003763362,0.0005155071,0.0002022887,0.9005128,0.05293613,0.006576151,0.03776744,0.00007835094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005712009,0.00008173254,0.9795736,0.00003588101,0.00005614605,0.00008109672,0.0003678954,0.01173912,0.002352615],"genre_scores_gemma":[0.117657,0.0001492845,0.8732279,0.00005834974,0.00002185429,0.0001591863,0.001974962,0.001990184,0.004761353],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01107703,"threshold_uncertainty_score":0.03705639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03460819347748425,"score_gpt":0.2118596479358707,"score_spread":0.1772514544583864,"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."}}