Interactive Isosurface Visualization in Memory Constrained Environments Using Deep Learning and Speculative Raycasting

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. 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, displaying intermediate results each pass. We improve the quality of these intermediate results using a pretrained deep neural network that reconstructs the output of early passes, allowing for interactivity with better approximates of the final image. 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 to exploit additional parallelism in the workload. Our algorithm is able to trade-off image quality to greatly decrease rendering time for interactive rendering even on lightweight devices. 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 $1.7\times$–$5.7\times$ reductions in memory overhead and up to $8.4\times$ reductions in data decompressed.