Stereo image warping for improved depth estimation of road surfaces

Accurate stereoscopic depth estimation, in particular of the road surface area, is one of several key technologies to improve Advanced Driver Assistance Systems (ADAS). One major problem is that the quality of the stereoscopic depth measurements of the road is often poor - which is mainly attributed to a lack of texture on the road surface. Especially for patch-matching stereo algorithms, the estimated depths look irregular and bumpy. In this paper, we show that the violation of the fronto-parallel assumption is the major reason for a bad depth estimation and not a low-contrast texture on the road surface. Since patch-matching or block-matching stereo inherently assumes a constant disparity within one patch, this is violated if the cameras are oriented almost parallel to the ground, which is typically the case in ADAS, and which leads to a strong distortion of the appearance between the two cameras. In order to tackle this problem, we propose a compensation of this distortion by applying a linear warp on one of the stereo images according to the expected disparity for the planar ground. This recovers the fronto-parallel assumption and results in a very good depth estimations of road surfaces. Our experiments on the KITTI stereo benchmark demonstrate the quantitative competitiveness of the approach, while retaining the speed and simplicity of block-matching stereo approaches. Furthermore, our experiments show that the approach is very robust, achieving results for the road surface that are significantly better than standard patch-matching stereo processing without warping for a wide range of warp parameter settings.