Abstract Segmentation volumes are voxel data sets often used in machine learning, connectomics, and natural sciences. Their large sizes make compression indispensable for storage and processing, including GPU video memory constrained real‐time visualization. Fast Compressed Segmentation Volumes (CSGV) [PD24] provide strong brick‐wise compression and random access at the brick level. Voxels within a brick, however, have to be decoded serially and thus rendering requires caching of visible full bricks, consuming extra memory. Without caching, accessing voxels can have a worst‐case decoding overhead of up to a full brick (typically over 32.000 voxels). We present CSGV‐R which provide true multi‐resolution random access on a per‐voxel level. We leverage Huffman‐shaped Wavelet Trees for random accesses to variable bit‐length encoding and their rank operation to query label palette offsets in bricks. Our real‐time segmentation volume visualization removes decoding artifacts from CSGV and renders CSGV‐R volumes without caching bricks at faster render times. CSGV‐R has slightly lower compression rates than CSGV, but outperforms Neuroglancer, the state‐of‐the‐art compression technique with true random access, with 2× to 4× smaller data sets at rates between 0.648% and 4.411% of the original volume sizes.

Voxel-based segmentation volumes often store a large number of labels and voxels, and the resulting amount of data can make storage, transfer, and interactive visualization difficult. We present a lossless compression technique which addresses these challenges. It processes individual small bricks of a segmentation volume and compactly encodes the labelled regions and their boundaries by an iterative refinement scheme. The result for each brick is a list of labels, and a sequence of operations to reconstruct the brick which is further compressed using rANS-entropy coding. As the relative frequencies of operations are very similar across bricks, the entropy coding can use global frequency tables for an entire data set which enables efficient and effective parallel (de)compression. Our technique achieves high throughput (up to gigabytes per second both for compression and decompression) and strong compression ratios of about 1% to 3% of the original data set size while being applicable to GPU-based rendering. We evaluate our method for various data sets from different fields and demonstrate GPU-based volume visualization with on-the-fly decompression, level-of-detail rendering (with optional on-demand streaming of detail coefficients to the GPU), and a caching strategy for decompressed bricks for further performance improvement.

Abstract In this paper, we present a novel method for the direct volume rendering of large smoothed‐particle hydrodynamics (SPH) simulation data without transforming the unstructured data to an intermediate representation. By directly visualizing the unstructured particle data, we avoid long preprocessing times and large storage requirements. This enables the visualization of large, time‐dependent, and multivariate data both as a post‐process and in situ. To address the computational complexity, we introduce stochastic volume rendering that considers only a subset of particles at each step during ray marching. The sample probabilities for selecting this subset at each step are thereby determined both in a view‐dependent manner and based on the spatial complexity of the data. Our stochastic volume rendering enables us to scale continuously from a fast, interactive preview to a more accurate volume rendering at higher cost. Lastly, we discuss the visualization of free‐surface and multi‐phase flows by including a multi‐material model with volumetric and surface shading into the stochastic volume rendering.