{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005585039,0.001623702,0.001140834,0.0008484538,0.0004028848,0.001379693,0.00254161,0.001646026,0.006164237],"category_scores_gemma":[0.001667517,0.0009910228,0.001403464,0.0006760036,0.0008420584,0.001577591,0.002396552,0.002259053,0.003514466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005499065,"about_ca_system_score_gemma":0.0009829159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009199547,"about_ca_topic_score_gemma":0.01378612,"domain_scores_codex":[0.9994178,0.00004742303,0.0000139279,0.000158049,0.0002963835,0.00006645681],"domain_scores_gemma":[0.999333,0.0001721859,0.00005084533,0.0002646621,0.0001199228,0.00005926974],"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":[0.0007820766,0.0003084753,0.003251845,0.0001968637,0.0001878517,0.0004566415,0.0002186901,0.3151109,0.04193501,0.006138194,0.01746494,0.6139483],"study_design_scores_gemma":[0.00001137284,0.00002684824,0.0002406185,0.000006822273,0.000007435357,0.00009041816,0.00001958427,0.9918436,0.004816042,0.001868869,0.001055584,0.00001262802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02620205,0.0002813585,0.9578267,0.0001980883,0.0001163628,0.00009090313,0.0006279079,0.01145873,0.003197948],"genre_scores_gemma":[0.3164476,0.0003924961,0.6700553,0.0004877513,0.00009580667,0.0001105709,0.004328106,0.001438241,0.006644047],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009199547,"threshold_uncertainty_score":0.02062136,"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."}}