{"id":"W4321854081","doi":"10.48550/arxiv.2302.11683","title":"MVTrans: Multi-View Perception of Transparent Objects","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Compute Canada; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Artificial intelligence; Computer vision; Computer science; RGB color model; Pipeline (software); Pose; Perception; Segmentation; Object (grammar); Robot; Depth perception; Object detection","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000177203,0.0002485944,0.0003640009,0.0002679704,0.00008444526,0.00003801725,0.001296013,0.0001410505,0.00002765792],"category_scores_gemma":[0.00001725939,0.0002796247,0.0002613382,0.000547375,0.00008651085,0.0003697696,0.0005524493,0.0003901082,0.0001082974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001095144,"about_ca_system_score_gemma":0.0001027456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009229819,"about_ca_topic_score_gemma":0.00003834392,"domain_scores_codex":[0.9983814,0.0001001327,0.0002432174,0.0008926679,0.0001101573,0.0002723869],"domain_scores_gemma":[0.9986134,0.00005503115,0.0001823104,0.0008981174,0.0001318965,0.0001192257],"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":[0.00006788231,0.0006679061,0.004300081,0.001616697,0.0002448768,0.0005874714,0.007002036,0.8702763,0.003791,0.0360446,0.0003004115,0.07510076],"study_design_scores_gemma":[0.0006224098,0.00004224611,0.00668961,0.0005009982,0.00004794306,0.000002164279,0.0003455901,0.9830217,0.0002507813,0.007844889,0.0002193203,0.0004123699],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01522975,0.00009930748,0.983128,0.00008869483,0.0005814318,0.000247559,0.00001451336,0.000335464,0.0002752499],"genre_scores_gemma":[0.9776505,0.001419402,0.01994406,0.00005815334,0.00002273506,8.535421e-7,0.00001312092,0.00001961289,0.0008715815],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.963184,"threshold_uncertainty_score":0.9999656,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.20160315753118,"score_gpt":0.2506157285634101,"score_spread":0.04901257103223006,"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."}}