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.
J. Cho, S. Ikehata, H. Yoo, M. Gelautz, K. Aizawa: "Depth Map Upsampling using Cost-Volume Filtering"; Talk: 11th IEEE IVMSP Workshop, Korea; 06-10-2013 - 06-12-2013; in: "Proc. of IVMSP Workshop", (2013), 1 - 4.
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