{"id":"W3083364047","doi":"10.20380/gi2020.35","title":"Fine Feature Reconstruction in Point Clouds by Adversarial Domain Translation","year":2020,"lang":"en","type":"article","venue":"Canada Human-Computer Communications Society","topic":"Image Processing and 3D Reconstruction","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Translation (biology); Point cloud; Adversarial system; Feature (linguistics); Computer science; Artificial intelligence; Point (geometry); Domain (mathematical analysis); Computer vision; Pattern recognition (psychology); Mathematics; Geometry; Linguistics","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":[],"consensus_categories":[],"category_scores_codex":[0.0002141048,0.0001919288,0.0002213286,0.00003535646,0.0005693306,0.000179727,0.001370093,0.0001345475,0.000009939449],"category_scores_gemma":[0.000005978095,0.0002145933,0.0001147231,0.0005941672,0.0001449607,0.0006117455,0.0002399203,0.0005743406,0.00000237705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003018648,"about_ca_system_score_gemma":0.0006593488,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01272152,"about_ca_topic_score_gemma":0.04416068,"domain_scores_codex":[0.998548,0.0001744981,0.0003685154,0.0004108515,0.0002356098,0.0002624876],"domain_scores_gemma":[0.9986564,0.00007865083,0.0001698746,0.0008648041,0.0001040778,0.0001261817],"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":[0.00001450802,0.0001312487,0.001081866,0.0001327888,0.0001202653,0.00000440078,0.01344782,0.0005954694,0.005677192,0.00664009,0.3742655,0.5978889],"study_design_scores_gemma":[0.003314975,0.0001003711,0.000976975,0.0001599137,0.0000282561,0.0001248262,0.0007588043,0.8217362,0.001399545,0.004326425,0.1660275,0.001046174],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008759462,0.001134973,0.9046361,0.08322725,0.0007229431,0.0003272409,0.00002239277,0.0002655485,0.0009040856],"genre_scores_gemma":[0.3211876,0.00006153698,0.6756077,0.002630365,0.0002672329,0.00002614521,0.0001378448,0.00001995156,0.00006159677],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8211407,"threshold_uncertainty_score":0.9938529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01940688153973725,"score_gpt":0.2293320048833532,"score_spread":0.209925123343616,"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."}}