Accelerating Cost Volume Filtering Using Salient Subvolumes and Robust Occlusion Handling

Abstract

Several fundamental computer vision problems, such as depth estimation from stereo, optical flow computation, etc., can be formulated as a discrete pixel labeling problem. Traditional Markov Random Fields (MRF) based solutions to these problems are computationally expensive. Cost Volume Filtering (CF) presents a compelling alternative. Still these methods must filter the entire cost volume to arrive at a solution. In this paper, we propose a new CF method for depth estimation by stereo. First, we propose the Accelerated Cost Volume Filtering (ACF) method which identifies salient subvolumes in the cost volume. Filtering is restricted to these subvolumes, resulting in significant performance gains. The proposed method does not consider the entire cost volume and results in a marginal increase in unlabeled (occluded) pixels. We address this by developing an Occlusion Handling (OH) technique, which uses superpixels and performs label propagation via a simulated annealing inspired method. We evaluate the proposed method (ACF+OH) on the Middlebury stereo benchmark and on high resolution images from Middlebury 2005/2006 stereo datasets, and our method achieves state-of-the-art results. Our occlusion handling method, when used as a post-processing step, also significantly improves the accuracy of two recent cost volume filtering methods.