An accurate hierarchical stereo matching method is proposed based on continuous 3D plane labeling of superpixel for rover’s stereo images. This method can infer the 3D plane label of each pixel combined with the slanted-patch matching strategy and coarse-to-fine constraints, which is especially suitable for large-scale scene matching with low-texture or textureless regions. At every level, the stereo matching method based on superpixel segmentation makes the iteration convergence faster and avoids huge redundant computations. In the coarse-to-fine matching scheme, we propose disparity constraint and 3D normal vector constraint between adjacent levels through which the disparity map and 3D normal vector map at a coarser level are used to restrict the search range of disparity and normal vector at a fine level. The experimental results with the Chang’e-3 rover dataset and the KITTI dataset show that the proposed stereo matching method is efficiently and accurately compared with the state-of-the-art 3D labeling algorithm, especially in low-texture or textureless regions. The computational efficiency of this method is about five to six times faster than the state-of-the-art 3D labeling method, and the accuracy is better.

提出了一种基于超像素连续3D平面标记的流动站立体图像的精确分层立体匹配方法。这种方法可以结合倾斜面匹配策略和粗到细约束来推断每个像素的3D平面标签，特别适用于具有低纹理或无纹理区域的大规模场景匹配。在每个级别上，基于超像素分割的立体匹配方法都使迭代收敛更快，并且避免了庞大的冗余计算。在粗到细匹配方案中，我们提出了相邻级别之间的视差约束和3D法向矢量约束，通过这些约束，可以使用较粗糙级别的视差图和3D法向矢量图来限制视差和法向矢量在范围内的搜索范围优良水平。使用Chang'e-3流动站数据集和KITTI数据集进行的实验结果表明，与最新的3D标记算法相比，尤其是在低纹理或无纹理的区域中，所提出的立体匹配方法是有效且准确的。这种方法的计算效率比最新的3D标记方法快约五到六倍，并且准确性更高。