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：自适应网格细化（AMR）正成为科学可视化中一种广泛使用的数据表示方式。源于大规模流体力学模拟的数据通常以单元为中心，这为在采样位置实现高质量重建带来了诸多挑战。尽管近期研究集中于GPU上的实时体绘制和等值面渲染，但所采用的渲染方法仍侧重于无散射事件和全局照明的简单光照模型。如同渲染领域的其他方面，实现实时性能的关键在于加速数据结构；本文分析了原本针对相机/主光线遍历优化的数据结构在体路径追踪的非相干光线追踪工作负载下所面临的主要瓶颈，并提出了应对这些挑战的策略。