Real-time stereo using approximated joint bilateral filtering and dynamic programming

We present a stereo algorithm that is capable of estimating scene depth information with high accuracy and in real time. The key idea is to employ an adaptive cost-volume filtering stage in a dynamic programming optimization framework. The per-pixel matching costs are aggregated via a separable implementation of the bilateral filtering technique. Our separable approximation offers comparable edge-preserving filtering capability and leads to a significant reduction in computational complexity compared to the traditional 2D filter. This cost aggregation step resolves the disparity inconsistency between scanlines, which are the typical problem for conventional dynamic programming based stereo approaches. Our algorithm is driven by two design goals: real-time performance and high accuracy depth estimation. For computational efficiency, we utilize the vector processing capability and parallelism in commodity graphics hardware to speed up this aggregation process over two orders of magnitude. Over 90 million disparity evaluations per second [the number of disparity evaluations per seconds (MDE/s) corresponds to the product of the number of pixels and the disparity range and the obtained frame rate and, therefore, captures the performance of a stereo algorithm in a single number] are achieved in our current implementation. In terms of quality, quantitative evaluation using data sets with ground truth disparities shows that our approach is one of the state-of-the-art real-time stereo algorithms.