Continuous Symmetric Stereo with Adaptive Outlier Handling

We present a method for symmetric stereo matching in which outliers from occlusions, texture-less regions, and repeated patterns are handled in a soft and adaptive manner. Rather than making binary outlier decisions, our model incorporates continuous-valued confidence weights that account for outlier likelihood, to promote robustness in disparity estimation. In contrast to previous outlier labeling techniques that fix the labels at the start of optimization, our method iteratively updates our outlier confidence weights as the matching results are gradually refined. By doing this, errors in an initial labeling can be rectified in the matching process. Our model is optimized in an Expectation-Maximization framework that efficiently produces continuous disparity estimates. This approach provides a good combination of accuracy and speed. Experiments show that our method compares favorably to prior outlier labeling techniques on the Middlebury benchmark, and that it can generate high-quality reconstruction for outdoor images with much more complex occlusions.