Binary descriptor-based dense line-scan stereo matching

Abstract

We present a line-scan stereo system and descriptor-based dense stereo matching for high-performance vision applications. The stochastic binary local descriptor (STABLE) descriptor is a local binary descriptor that builds upon the principles of compressed sensing theory. The most important properties of STABLE are the independence of the descriptor length from the matching window size and the possibility that more than one pair of pixels contributes to a single-descriptor bit. Individual descriptor bits are computed by comparing image intensities over pairs of balanced random subsets of pixels chosen from the whole described area. On a synthetic as well as real-world examples, we demonstrate that STABLE provides competitive or superior performance than other state-of-the-art local binary descriptors in the task of dense stereo matching. The real-world example is derived from line-scan binocular stereo imaging, i.e., two line-scan cameras are observing the same object line and 2-D images are generated due to relative motion. We show that STABLE performs significantly better than the census transform and local binary patterns (LBP) in all considered geometric and radiometric distortion categories to be expected in practical applications of stereo vision. Moreover, we show as well that STABLE provides comparable or better matching quality than the binary robust-independent elementary features descriptor. The low computational complexity and flexible memory footprint make STABLE well suited for most hardware architectures. We present quantitative results based on the Middlebury stereo dataset as well as illustrative results for road surface reconstruction.