Among the most exciting advances in early vision has been the development of efficient energy
minimization algorithms for pixel-labeling tasks such as depth or texture computation.
It has been known for decades that such problems can be elegantly expressed as Markov random fields,
yet the resulting energy minimization problems have been widely viewed as intractable. Recently,
algorithms such as graph cuts and loopy belief propagation (LBP) have proven to be very powerful:
for example, such methods form the basis for almost all the top-performing stereo methods. However,
the tradeoffs among different energy minimization algorithms are still not well understood. In this
paper we describe a set of energy minimization benchmarks and use them to compare the solution
quality and running time of several common energy minimization algorithms. We investigate three promising
recent methods - graph cuts, LBP, and tree-reweighted message passing - in addition to the well-known older
iterated conditional modes (ICM) algorithm. Our benchmark problems are drawn from published energy
functions used for stereo, image stitching, interactive segmentation, and denoising.
We also provide a general-purpose software interface that allows vision researchers to easily
switch between optimization methods. Benchmarks, code, images, and results are available at
http://vision.middlebury.edu/MRF.