Dense Correspondence of 3d Facial Point Clouds Via Neural Network Fitting
Weihao Zhou, Chenxi Zhao, Lu Li, Qijun Zhao · 2019
3D face dense correspondence is an important and challenging problem in 3D face analysis. Previous methods usually solve this problem by applying transformations to align different 3D faces and are thus constrained by the employed specific transformations. In this paper, instead, we approach the problem as a surface fitting and re-sampling problem. Modelling the point cloud of a 3D face as a surface, we use a neural network to fit the surface, and evaluate the obtained network at pre-specified sampling points that are defined based on prior knowledge of face structure. We evaluate the proposed method on the BU-3DFE database and prove its effectiveness both qualitatively and quantitatively.