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Convolutional neural networks, which estimate depth from stereo pictures in a single step, have become state of the art recently. The search space for matching pixels is hard coded in these networks and in literature is chosen to be the disparity space, corresponding to a search in the cameras viewing direction. In the proposed method, the search space is altered by a plane sweep approach, reducing necessary search steps for depth map estimation of flat surfaces. The described method is shown to provide high quality depth maps of road surfaces in the targeted application of pavement distress detection, where the stereo cameras are mounted behind the windshield of a moving vehicle. It provides a cheap replacement for laser scanning for this purpose.

Incorporating Plane-Sweep in Convolutional Neural Network Stereo Imaging for Road Surface Reconstruction