{"id":"W4287990769","doi":"10.48550/arxiv.1912.10589","title":"Front2Back: Single View 3D Shape Reconstruction via Front to Back\\n Prediction","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":3,"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":"Computer science; Artificial intelligence; Ambiguity; Silhouette; Benchmark (surveying); 3D reconstruction; Computer vision; Perspective (graphical); Surface (topology); Point cloud; Surface reconstruction; Depth map; Geometry; Image (mathematics); Mathematics; Geology","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001366075,0.0003868636,0.0004987814,0.0003601054,0.0000786079,0.00006382298,0.0003755109,0.0004184779,0.0007982823],"category_scores_gemma":[0.0000110691,0.0004824125,0.0002856413,0.0002741151,0.00003023969,0.0001920868,0.0002420927,0.0005345385,0.00194073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005988827,"about_ca_system_score_gemma":0.00004003306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001250679,"about_ca_topic_score_gemma":0.0001087706,"domain_scores_codex":[0.9984137,0.00005653069,0.0003015408,0.000792407,0.00009090851,0.0003449569],"domain_scores_gemma":[0.9988412,0.00002318654,0.00009799924,0.0007268574,0.0001210069,0.0001897647],"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":[0.00001596448,0.00002803893,0.0008022421,0.000174328,0.0002357812,0.00001014473,0.00009280416,0.9811178,0.0002364559,0.00001642185,0.0007380497,0.01653199],"study_design_scores_gemma":[0.0002372007,0.0000377734,0.0001965964,0.0003143225,0.0002851906,0.000004340364,0.00004629702,0.9968193,0.0001078947,0.0004838026,0.001012995,0.0004543583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2750014,0.0002446135,0.7150071,0.00001852002,0.001578665,0.0002775506,0.00007411965,0.0004606333,0.007337414],"genre_scores_gemma":[0.9950067,0.0004131636,0.00172064,0.00003747288,0.0002303417,0.000001466642,0.0001032952,0.00006303593,0.002423893],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7200053,"threshold_uncertainty_score":0.9997628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04338419985136831,"score_gpt":0.156265947216648,"score_spread":0.1128817473652797,"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."}}