Pose-Aware Smoothing Filter for Depth Images

Abstract

We propose a novel smoothing algorithm for depth images. Inspired by a bilateral filter, we design a pose-aware smoothing filter for sequentially captured multiple depth images. First, our method solves the frame-to-frame tracking problem by using the point-to-plane iterative corresponding point (ICP) algorithm between a keyframe depth image and multiple nearby depth images, which gives us relative transforms between camera poses. Then, we re-project those depth images onto a keyframe depth image by using previously computed relative transforms. Finally, we merge those depth images onto a keyframe depth image by applying a proposed smoothing filter. Since our kernel function uses pixel distance and information similarities not in one depth image but in several depth images that are temporally nearby, it is more robust to noise while preserving geometric details than a conventional bilateral filter. Our smoothing algorithm can be combined with the traditional bilateral filter to take advantages of both algorithms. Additionally, our algorithm can be applied on dynamic scenes by detecting dynamic pixels and removing dynamic values. We present experiments with depth images captured by a depth camera.