IEEE International Conference on Biometrics, Theory, Applications and Systems. IEEE Conference on Biometrics: Theory, Applications, and Systems最新文献

筛选

英文中文

Meta-evaluation for 3D Face Reconstruction Via Synthetic Data. 通过合成数据进行 3D 人脸重建的元评估。

IEEE International Conference on Biometrics, Theory, Applications and Systems. IEEE Conference on Biometrics: Theory, Applications, and Systems Pub Date : 2023-09-01 DOI: 10.1109/ijcb57857.2023.10448898

Evangelos Sariyanidi, Claudio Ferrari, Stefano Berretti, Robert T Schultz, Birkan Tunc

{"title":"Meta-evaluation for 3D Face Reconstruction Via Synthetic Data.","authors":"Evangelos Sariyanidi, Claudio Ferrari, Stefano Berretti, Robert T Schultz, Birkan Tunc","doi":"10.1109/ijcb57857.2023.10448898","DOIUrl":"10.1109/ijcb57857.2023.10448898","url":null,"abstract":"<p><p>The standard benchmark metric for 3D face reconstruction is the geometric error between reconstructed meshes and the ground truth. Nearly all recent reconstruction methods are validated on real ground truth scans, in which case one needs to establish point correspondence prior to error computation, which is typically done with the Chamfer (i.e., nearest neighbor) criterion. However, a simple yet fundamental question have not been asked: Is the Chamfer error an appropriate and fair benchmark metric for 3D face reconstruction? More generally, how can we determine which error estimator is a better benchmark metric? We present a meta-evaluation framework that uses synthetic data to evaluate the quality of a geometric error estimator as a benchmark metric for face reconstruction. Further, we use this framework to experimentally compare four geometric error estimators. Results show that the standard approach not only severely underestimates the error, but also does so inconsistently across reconstruction methods, to the point of even altering the ranking of the compared methods. Moreover, although non-rigid ICP leads to a metric with smaller estimation bias, it could still not correctly rank all compared reconstruction methods, and is significantly more time consuming than Chamfer. In sum, we show several issues present in the current benchmarking and propose a procedure using synthetic data to address these issues.</p>","PeriodicalId":519906,"journal":{"name":"IEEE International Conference on Biometrics, Theory, Applications and Systems. IEEE Conference on Biometrics: Theory, Applications, and Systems","volume":"2023 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11086654/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140912740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0