{"id":"W2133803071","doi":"10.1109/tbc.2005.846190","title":"Stereoscopic Image Generation Based on Depth Images for 3D TV","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Broadcasting","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":505,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Computer vision; Image warping; Stereoscopy; Artificial intelligence; Rendering (computer graphics); Computer science; Image-based modeling and rendering; View synthesis; Distortion (music); Smoothing; Virtual image; Image quality; Computer graphics (images); Depth map; Image-based lighting; Image (mathematics); Bandwidth (computing)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002112143,0.0003747519,0.000320414,0.0004608872,0.0001979695,0.0004115987,0.0006551997,0.0003995929,0.004599549],"category_scores_gemma":[0.0005064057,0.0002685576,0.0003561796,0.0002571866,0.0001909772,0.0003241941,0.0004354136,0.0005457909,0.0007908014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002491223,"about_ca_system_score_gemma":0.0002637111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008158339,"about_ca_topic_score_gemma":0.001114672,"domain_scores_codex":[0.9998079,0.00003060451,0.00000685833,0.00002117727,0.0001141072,0.00001932568],"domain_scores_gemma":[0.9997894,0.00005507939,0.00002118746,0.00003947429,0.00007220102,0.00002257619],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002895075,0.00007849658,0.0006716455,0.0002550311,0.00003205905,0.0003640194,0.0002712646,0.01152036,0.6643052,0.009078116,0.005294327,0.30784],"study_design_scores_gemma":[0.00017744,0.0005060121,0.0026385,0.00005457329,0.00007891125,0.002337465,0.00006806157,0.4124864,0.536046,0.00336868,0.0421083,0.0001296413],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02018773,0.0002125203,0.9727746,0.0001101012,0.00008043752,0.0001307092,0.0001379271,0.002210339,0.004155695],"genre_scores_gemma":[0.2453824,0.0003847464,0.7478252,0.000137133,0.00009243684,0.0001360274,0.0002866291,0.0002405597,0.005514992],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004599549,"threshold_uncertainty_score":0.015387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03574500606888086,"score_gpt":0.3039468370017477,"score_spread":0.2682018309328668,"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."}}