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.
翻译:由于大型流体力学模拟,数据通常以细胞为中心,给抽样位置的高质量重建带来了若干挑战。虽然最近的工作集中于实时量和GPU的表层覆盖,但所使用的转换方法仍然侧重于简单的照明模型,而没有分散事件和全球照明。与在其他领域一样,实时性能的关键是加速数据结构;在这项工作中,我们分析了数据结构的主要瓶颈,在与量子追踪器的不连贯的射线跟踪工作量同时使用时,这些结构最初被优化用于照相机/初级射线穿透系统,并提出了克服随之而来的挑战的战略。