Consistent Visualization For Surface Vector Fields

Image-space line integral convolution (LIC) is a popular approach for visualizing surface vector fields due to its simplicity and high efficiency. But it may lead to inconsistencies or color blur during the user interaction because the noise texture is generated independently in each frame and has inadequate resolution. Existing methods rely on surface parameterization to achieve global consistency which lacks of the efficiency of the image-space approaches.This paper introduces a novel image-space LIC for surface flows which preserves the texture coherence during user interactions. To make the noise textures under different viewpoints coherent, we propose a simple texture mapping technique which is local, robust and effective. Meanwhile, our approach pre-computes a sequence of mipmap noise images in a coarse-to-fine manner, leading to consistent transition when the model is zoomed. Prior to perform LIC in the image space, a noise image at a selected mipmap level is mapped onto each triangle with randomly assigned texture coordinates. Then, a standard image-space LIC based on the projected vector fields is performed to generate the flow texture. The proposed approach is simple and very suitable for GPU acceleration. Our implementation demonstrates consistent and highly efficient LIC visualization with a variety of datasets.