3D Face Point Cloud Super-Resolution Network

Abstract

With the development of consumer-level depth sensors, 3D face point cloud data can be easily captured now. However, such data are often accompanied by low resolution, noise, and holes. At the same time, high-precision 3D scanners are bulky and can not be widely used in daily applications due to costs and inconvenience. To fill the gap between low and high resolution 3D faces, we propose a two-stage framework named the face point cloud super-resolution network (FPSRN) to recover high-resolution 3D face data from the low-resolution counterparts. As the human faces can be aligned into a unified coordinate system, we formulate point cloud super-resolution as a z-coordinate prediction problem. Cascaded auto-encoders are employed to retain both global structure and boundary information of different face regions during super-resolution. Compared with state- of-the-art point cloud completion methods and depth estimation methods, our method improves the Earth-Mover’s Distance (EMD) and the Root Mean Square Error (RMSE) metrics by 43% and 25%, respectively.