Deep-Learning-Based Multiview RGBD Sensor System for 3-D Face Point Cloud Registration

Abstract

Three-dimensional face analysis is now a hot research topic in computer vision. The acquisition of 3D faces requires acquiring multi-view face and obtaining them by point cloud registration. In this paper, a deep learning method is introduced in the multi-view RGBD depth sensor system to acquire matching point pairs of image data. Thus, 3D spatial point pairs are obtained. Then, the initial poses between the point clouds of multi-view faces are obtained by rigid body transformation estimation, and the coarse registration of the point clouds is completed. On this basis, for fine registration of multi-view 3D face point clouds using the Iterative Closest Point (ICP) algorithm, this paper proposes a method to convert partially overlapping point cloud registration into sub-region point cloud registration through facial region positioning. In this letter, the maximum value of RMSE for the experimental results of point cloud coarse registration is 2.26 mm with the mean and standard deviation of 1.18 ± 0.33 mm; the maximum value of RMSE for the experimental results of point cloud fine registration is 1.47 mm with the mean and standard deviation of 0.91 ± 0.14 mm. It is confirmed that the proposed method is very robust and effective for the registration of multi-view 3D face point clouds.