基于GPU的非结构化网格数据体光照计算与实现方法

光照在提高体绘制质量方面发挥重要作用,而梯度计算是实现体光照的关键.与结构化网格相比,非结构化网格拓扑关系复杂,使得顶点梯度估计困难,采样点梯度计算复杂度高,且不易采用GPU加速,阻碍了算法的实时性.因此,绝大多数非结构化网格体绘制尚未添加体光照.本文提出一种高精度的非结构化网格顶点梯度计算方法:先采用格林公式估计单元...

GPU-Based Computation and an Implementation Method of Volume Illumination for Unstructured-Grid Data

Gradient estimation is necessary for illumination effects which play an important role in volume rendering. Compared with the structured grids, it is difficult to estimate the vertex gradient for unstructured grids due to the complicated topology. Futhermore,real time rendering can be hardly achieved due to the computation complexity of the resampled point gradient and the difficultity of implemention on GPUs. As a result, most of the unstructured grid volumes have not been lit. This paper presents a method to estimate the vertex gradient with high precision for unstructured grids. It employs the volume weighted extrapolation and the inverse distance extrapolation to compute a vertex gradient using a group of cell gradients estimated by the Green theorem. Meanwhile, to compute the gradient at a resampled point, we present an efficient method based on the cell divergence which makes the cost much lower than the recent method of full linear interpolation。The real time performance of our algorithm even for the relative large data sets can be achieved by its GPU implementaion with the aid of a well designed data structure.