Ray-Based Exploration of Large Time-Varying Volume Data Using Per-Ray Proxy Distributions

The analysis and visualization of data created from simulations on modern supercomputers is a daunting challenge because the incredible compute power of modern supercomputers allow scientists to generate datasets with very high spatial and temporal resolutions. The limited bandwidth and capacity of networking and storage devices connecting supercomputers to analysis machines become the major bottleneck for data analysis such that simply moving the whole dataset from the supercomputer to a data analysis machine is infeasible. A common approach to visualize high temporal resolution simulation datasets under constrained I/O is to reduce the sampling rate in the temporal domain while preserving the original spatial resolution at the time steps. Data interpolation between the sampled time steps alone may not be a viable option since it may suffer from large errors, especially when using a lower sampling rate. We present a novel ray-based representation storing ray based histograms and depth information that recovers the evolution of volume data between sampled time steps. Our view-dependent proxy allows for a good trade off between compactly representing the time-varying data and leveraging temporal coherence within the data by utilizing interpolation between time steps, ray histograms, depth information, and codebooks. Our approach is able to provide fast rendering in the context of transfer function exploration to support visualization of feature evolution in time-varying data.