Figure 1: Starry Night: Starting with two diffusion curve images (a) and (b), the moon in (b) is cloned into (a), producing (c). Diffusion points are added to simulate stars (d) and an aurora (e). For the reflection on the lake (f), the hue is shifted towards green. Finally, (e) and (f) are composited with a mask to produce the final image on the right. Note that the reflection is darker away from the horizon. All these editing operations were performed on the fast multipole representation. We propose a new algorithm for random-access evaluation of diffusion curve images (DCIs) using the fast multipole method. Unlike all previous methods, our algorithm achieves real-time performance for rasterization and texture-mapping DCIs of up to millions of curves. After precomputation, computing the color at a single pixel takes nearly constant time. We also incorporate Gaussian radial basis functions into our fast multipole representation using the fast Gauss transform. The fast multipole representation is not only a data structure for fast color evaluation, but also a framework for vector graphics analogues of bitmap editing operations. We exhibit this capability by devising new tools for fast diffusion curve Poisson cloning and composition with masks.
REF introduced a fast multipole representation for rendering diffusion curve images in real-time.
Fast multipole representation of diffusion curves and points
TOGS
Computer Science