Performance of MRF-Based Stereo Algorithms for Cluttered Scenes
This paper evaluates the performance of different Markov Random Field (MRF) based stereo algorithms for cluttered scenes. These scenes are generated by randomly placing objects within a 3D volume. The scenes, which model natural cluttered scenes such as forests or bushes, contain many depth discontinuities and monocularly visible pixels. Widely used benchmark datasets do not contain stereo pairs with dense clutter, so we address how well existing stereo algorithms perform for such scenes. We use Expansion, Swap, Max Product Belief Propagation (BPM), Sequential Tree Reweighted Message Passing (TRW-S) and Sequential Belief Propagation (BP-S), all with different forms of data and smoothness terms. The results are compared with the ground truth disparity and energy.We found Expansion, TRW-S, and BP-M with the Potts model to work well for most scenes, in that the correct binocular correspondence is found for most points that are binocularly visible. We also found that the energy for the ground truth is much larger than what is found by the optimizers. This shows that there is room for improving the model for cluttered scenes.
KeywordsMarkov Random Field Binocular Visibility Stereo Pair Scene Category Depth Discontinuity
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