| Qifeng Chen | Vladlen Koltun |
| IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 | |
Abstract
We describe a simple and fast algorithm for optimizing Markov random fields over images. The algorithm performs block coordinate descent by optimally updating a horizontal or vertical line in each step. While the algorithm is not as accurate as state-of-the-art MRF solvers on traditional benchmark problems, it is trivially parallelizable and produces competitive results in a fraction of a second. As an application, we develop an approach to increasing the accuracy of consumer depth cameras. The presented algorithm enables high-resolution MRF optimization at multiple frames per second and substantially increases the accuracy of the produced range images.
Materials
Paper
Supplementary material
Poster
Code for fast MRF optimization (Section 2)
Code for depth reconstruction (Section 4)
Ground-truth 3D models (Section 5.1)
Ground-truth 3D models without holes (thanks to Benjamin Ummenhofer)
Test data for quantitative evaluation of depth reconstruction (Section 5.1)
Updated base pattern (updated on 10 March, 2019)
For the updated data, there are three base speckle patterns I captured at different ranges, "base.mat" for short range, "base_mid.mat" for mid-range, and "base_long.mat" for long range. These patterns are for the types of IR images without black rows. For the base speckle patterns I put on the project website, they are for the type with black rows. You can run test.m in Windows to get the cost volume stored in "table?.mat". I visualize a row of cost volume, and it looks good to me. I did not notice there were two types of IR images at the beginning of the project, so I accumulated two types of IR images over time.
Note
After publication we discovered that block coordinate descent on the primal MRF objective was also discussed in the following paper:
Kelm, B.M.; Mueller, N.; Menze, B.H.; Hamprecht, F.A.,
"Bayesian Estimation of Smooth Parameter Maps for Dynamic Contrast-Enhanced MR Images with Block-ICM,"
Computer Vision and Pattern Recognition Workshop, 2006. CVPRW '06.