Depth map up-sampling using cost-volume filtering

Abstract

Depth maps captured by active sensors (e.g., ToF cameras and Kinect) typically suffer from poor spatial resolution, considerable amount of noise, and missing data. To overcome these problems, we propose a novel depth map up-sampling method which increases the resolution of the original depth map while effectively suppressing aliasing artifacts. Assuming that a registered high-resolution texture image is available, the cost-volume filtering framework is applied to this problem. Our experiments show that cost-volume filtering can generate the high-resolution depth map accurately and efficiently while preserving discontinuous object boundaries, which is often a challenge when various state-of-the-art algorithms are applied.