Temporally consistent disparity estimation using PCA dual-cross-bilateral grid

Disparity estimation has been extensively investigated in recent years. Though several algorithms have been reported to achieve excellent performance on the Middlebury website, few of them reach a satisfying balance between accuracy and efficiency, and few of them consider the problem of temporal coherence. In this paper, we introduce a novel disparity estimation approach, which improves the accuracy for static images and the temporal coherence for videos. For static images, the proposed approach is inspired by the adaptive support weight method proposed by Yoon et al. and the dual-cross-bilateral grid introduced by Richardt et al. Principal component analysis (PCA) is used to reduce the color dimensionality in the cost aggregation step. This simple, but efficient technique helps the proposed method to be comparable to the best local algorithms on the Middlebury website, while still allowing real-time implementation. A computationally efficient method for temporally consistent behavior is also proposed. Moreover, in the user evaluation experiment, the proposed temporal approach achieves the best overall user experience among the selected comparison algorithms.