论文标题

超越Exabricks:AMR数据的GPU音量路径跟踪

Beyond ExaBricks: GPU Volume Path Tracing of AMR Data

论文作者

Zellmann, Stefan, Wu, Qi, Sahistan, Alper, Ma, Kwan-Liu, Wald, Ingo

论文摘要

自适应网状细化(AMR)正在成为科学可视化的普遍数据表示。由于大型流体力学模拟,数据通常以细胞为中心,对样本位置的高质量重建构成了许多挑战。虽然最近的工作集中在GPU上的实时量和等字渲染上,但使用的渲染方法仍然集中在简单的照明模型上,而无需散射事件和全球照明。与其他渲染的其他领域一样,实时性能的关键是加速数据结构。在这项工作中,我们分析了最初针对摄像机/初级射线遍历优化的数据结构的主要瓶颈,与体积路径示踪剂的不连贯的射线示踪工作量一起使用,并提出了克服这一挑战的策略。

Adaptive Mesh Refinement (AMR) is becoming a prevalent data representation for scientific visualization. Resulting from large fluid mechanics simulations, the data is usually cell centric, imposing a number of challenges for high quality reconstruction at sample positions. While recent work has concentrated on real-time volume and isosurface rendering on GPUs, the rendering methods used still focus on simple lighting models without scattering events and global illumination. As in other areas of rendering, key to real-time performance are acceleration data structures; in this work we analyze the major bottlenecks of data structures that were originally optimized for camera/primary ray traversal when used with the incoherent ray tracing workload of a volumetric path tracer, and propose strategies to overcome the challenges coming with this.

扫码加入交流群

加入微信交流群

微信交流群二维码

扫码加入学术交流群,获取更多资源