Laplacian Line Drawing Algorithm Based on Difference-of-Gaussian Approximation

In recent years, image-space line drawing techniques have made considerable progress in terms of quality and robustness. However, image-space methods do not generate the feature lines geometry in object space, which limits their application in many aspects. On the other hand, the rendering quality and efficiency of current object-space line drawing algorithms are inferior to the image-space methods. In this paper we present a new object-space line drawing algorithm. We use the Difference-of-Gaussian（Do G） operator for mesh surfaces to precompute the Laplacian of normal for each vertex. At runtime, the feature lines are efficiently extracted by computing the dot product of the Laplacian of normal to the viewing direction. Taking the direction of features into consideration, we further employ the anisotropic Do G operator, which filters differently in the gradient and tangent directions at each point. Experimental results show that, our method is more robust than the original Laplacian line drawing algorithm, as well as other object-space algorithms. For models with geometric noises and irregular tessellation, our results are smoother and cleaner. In addition, our method has the same run-time efficiency as the original Laplacian lines, and reaches real-time performance for models with millions of triangles.