Memory-Efficient Parametric Semiglobal Matching

Accurate stereo matching for depth extraction requires a large memory space, which restricts its use in resource-limited systems. The problem is aggravated by the recent trend of applications requiring significantly high pixel resolution and disparity levels. To alleviate the high memory requirement, we propose to represent the aggregation costs as a Gaussian mixture model (GMM) function. Only a set of GMM parameters is stored and used instead of all the costs for each pixel. We also propose GMM parameter update-based aggregation along multiple paths. To preserve the accuracy of the disparity map, we employ a depth confidence measure and propose an update rule for the slanted surface of an object. Experimental results over the KITTI dataset show that the proposed method reduces the memory requirement to less than 5% of that of semiglobal matching, while the accuracy is maintained at the level of state-of-the-art semiglobal and local methods.