A unified model for improving depth accuracy in kinect sensor

Abstract

The Microsoft Kinect sensor has been widely used in many applications, but it suffers from the drawback of low depth accuracy. In this paper, we present a unified depth modification model to improve the Kinect depth accuracy by registering depth and color images in an iterative manner. Specifically, in each iteration, we first establish a coarse correspondence based on the feature descriptor of the canny edge. Then, we estimate the fine correspondence using a robust estimator called the L2E with the nonparametric model. Finally, we correct the depth data according to the correspondence results. In order to evaluate the effectiveness of our approach, we have performed extensive experiments and then analyzed the experimental results from the following respects: the accuracy of depth data, the accuracy of correspondence between color and depth images as well as the measurement error in the 3D reconstruction by our method. The experimental results show that our approach greatly improves the depth accuracy.