提出了一种基于稀疏体素有向无环图(SVDAG)的光照计算加速结构。通过自下而上地合并相同节点,将稀疏体素八叉树转化为SVDAG;同时,利用给定节点的遍历路径和子掩码,消除了空间位置的多义性。针对闭合几何体,基于双层深度图算法可自适应合并位于闭合区域的节点,在保证光照计算性能的前提下,进一步减小了存储开销。提出了一种基于时域相关性的SVDAG帧间复用方法,利用动态场景的全部帧构成SVDAG加速结构整体,提高了更新速度。实验结果表明,新算法提高了三维场景的绘制效率,在面对高分辨率的动态场景时,仍能获得较高的帧速率。

An illumination-computation acceleration structure based on the sparse voxel directed acyclic graph (SVDAG) is proposed. By merging the same nodes from bottom to top, the sparse voxel octree is converted into a SVDAG, and the polysemy of spatial positions can be eliminated by using the traversal paths and the child masks of the given nodes. Aiming at the closed geometry, an algorithm based on the double depth maps can be used to merge adaptively the nodes located in the closed region, which can further reduce the storage cost while the performance of illumination computation is maintained. A inter-frame multiplex method of SVDAG based on the time correlation is proposed in which all frames of the dynamic scene are used to constitute an integral SVDAG acceleration structure, which can improve the update rate. The experimental results indicate that the rendering efficiency of three-dimensional scene based on the new algorithm is enhanced. When a high resolution dynamic scene is conducted, a relatively high frame rate still can be obtained.