Fast MRF Optimization with Application to Depth Reconstruction

Qifeng Chen Vladlen Koltun
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014


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.


Supplementary material
Code for fast MRF optimization (Section 2)
Code for depth reconstruction (Section 4)
Ground-truth 3D models (Section 5.1)
Test data for quantitative evaluation of depth reconstruction (Section 5.1)

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.