New web technologies have enabled the deployment of powerful GPU-based computational pipelines that run entirely in the web browser, opening a new frontier for accessible scientific visualization applications. However, these new capabilities do not address the memory constraints of lightweight end-user devices encountered when attempting to visualize the massive data sets produced by today's simulations and data acquisition systems. In this paper, we propose a novel implicit isosurface rendering algorithm for interactive visualization of massive volumes within a small memory footprint. We achieve this by progressively traversing a wavefront of rays through the volume and decompressing blocks of the data on-demand to perform implicit ray-isosurface intersections. The progressively rendered surface is displayed after each pass to improve interactivity. Furthermore, to accelerate rendering and increase GPU utilization, we introduce speculative ray-block intersection into our algorithm, where additional blocks are traversed and intersected speculatively along rays as other rays terminate to exploit additional parallelism in the workload. Our entire pipeline is run in parallel on the GPU to leverage the parallel computing power that is available even on lightweight end-user devices. We compare our algorithm to the state of the art in low-overhead isosurface extraction and demonstrate that it achieves 1.7x-5.7x reductions in memory overhead and up to 8.4x reductions in data decompressed.

翻译：新的Web技术使得完全在浏览器中运行的基于GPU的强大计算管线得以部署，为可访问的科学可视化应用开辟了新前沿。然而，当试图可视化当今模拟与数据采集系统产生的大规模数据集时，这些新能力并未解决轻量级终端设备的内存限制问题。本文提出了一种新颖的隐式等值面渲染算法，可在小内存占用下实现大规模体数据的交互式可视化。我们通过渐进式遍历穿过体数据的光线波前，并按需解压数据块以执行隐式光线-等值面求交来实现这一目标。每轮遍历后显示渐进渲染的表面以提高交互性。此外，为加速渲染并提高GPU利用率，我们在算法中引入推测性光线-块求交——当其他光线终止时，沿光线方向对额外数据块进行推测性遍历与求交，以充分利用工作负载中的并行性。整个管线在GPU上并行运行，以利用轻量级终端设备上可用的并行计算能力。我们将算法与低开销等值面提取领域的最新成果进行对比，证明其能实现1.7倍至5.7倍的内存开销缩减，以及最高8.4倍的数据解压缩量缩减。