Abstract

We propose a novel regularization model for stereo matching that uses large neighborhood windows. The model is based on the observa- tion that in a local neighborhood there exists a linear relationship between pixel values and disparities. Compared to the traditional boundary pre- serving regularization models that use adjacent pixels, the proposed model is robust to image noise and captures higher level interactions. We develop a globally optimized stereo matching algorithm based on this regulariza- tion model. The algorithm alternates between finding a quadratic upper bound of the relaxed energy function and solving the upper bound using iterative reweighted least squares. To reduce the chance of being trapped in local minima, we propose a progressive convex-hull filter to tighten the data cost relaxation. Our evaluation on the Middlebury datasets shows the effectiveness of our method in preserving boundary sharpness while keeping regions smooth. We also evaluate our method on a wide range of challenging real-world videos. Experimental results show that our method outperforms existing methods in temporal consistency.