Shape-indifferent stereo disparity based on disparity gradient estimation

Abstract

A new algorithm capable of estimating disparity gradients to produce accurate dense disparities is proposed. Such a disparity gradient plays a critical role in acquiring accurate disparities for scenes with many different object shapes. The target is a road traffic scene because it contains various objects, including the road surface, vehicles, pedestrians, sidewalks, and walls. In this paper, we adopt several methods, such as initial matching cost computation, scanline optimization, left/right consistency check, and cost aggregation. However, disparity accuracy is slightly improved by the simple organization of such methods. Disparity quality decisively relies on the application of disparity gradients. Accordingly, in the proposed algorithm, cost aggregation is performed along the direction of the estimated disparity gradient in a disparity space image. This approach improves disparity quality significantly. However, this cost aggregation is time consuming. To reduce the time required, we designed a new 2D integral cost technique. The robustness of the proposed algorithm is demonstrated through the disparity maps obtained from standard images on the Web, indoor images, and outdoor images of various road traffic scenes.