Talks and Poster Presentations (with Proceedings-Entry):
J. Cho, S. Ikehata, H. Yoo, M. Gelautz, K. Aizawa:
"Depth Map Upsampling using Cost-Volume Filtering";
Talk: 11th IEEE IVMSP Workshop,
- 2013-06-12; in: "Proc. of IVMSP Workshop",
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.
Depth map super-resolution, cost-volume filtering, up-sampling
"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
Project Head Margrit Gelautz:
Created from the Publication Database of the Vienna University of Technology.