Depth Map Upsampling using Cost-Volume Filtering

J. Cho, S. Ikehata, H. Yoo, M. Gelautz, K. Aizawa:
"Depth Map Upsampling using Cost-Volume Filtering";
Vortrag: 11th IEEE IVMSP Workshop, Korea; 10.06.2013 - 12.06.2013; in:"Proc. of IVMSP Workshop", (2013), S. 1 - 4.

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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.