Computer vision-based crack detection methods for large-scale civil structures are mainly developed within the framework of image enhancement and segmentation. In contrast, we propose an idea that converts the images into parametric surfaces and then detects the crack surfaces using shape recognition techniques. The shape variations among noncrack surfaces are caused by approximately isometric deformations, but the dissimilarities between crack and noncrack surfaces are produced by nonisometric deformations. Therefore, the two classes of surfaces can be discriminated by their geodesic distance maps. To tackle the disturbances caused by the gaps in cracks, we develop a dedicated method, steady marching method, for the computation of the distance maps. In the subsequent quantitative comparison, we first construct distance difference matrices from the distance maps. Next, sub-block contrast ratios of these matrices are calculated and used as shape descriptors, which can be directly compared for the surface classification. Experimental results demonstrate that our method achieves a better performance than some typical methods. The extension of our method for crack localization is also presented.