Evaluation of Dense 3D Reconstruction from 2D Face Images in the Wild
It provides a standardized evaluation framework for researchers in computer vision and face recognition, but it is incremental as it builds on existing competitions by focusing on real, high-resolution data.
This paper tackles the problem of evaluating dense 3D face reconstruction from single 2D images in the wild by organizing a competition with a new benchmark dataset of 2000 images and 3D ground truth scans, reporting results from three state-of-the-art systems.
This paper investigates the evaluation of dense 3D face reconstruction from a single 2D image in the wild. To this end, we organise a competition that provides a new benchmark dataset that contains 2000 2D facial images of 135 subjects as well as their 3D ground truth face scans. In contrast to previous competitions or challenges, the aim of this new benchmark dataset is to evaluate the accuracy of a 3D dense face reconstruction algorithm using real, accurate and high-resolution 3D ground truth face scans. In addition to the dataset, we provide a standard protocol as well as a Python script for the evaluation. Last, we report the results obtained by three state-of-the-art 3D face reconstruction systems on the new benchmark dataset. The competition is organised along with the 2018 13th IEEE Conference on Automatic Face & Gesture Recognition.