A framework for GPU‐accelerated exploration of massive time‐varying rectilinear scalar volumes

Abstract

We introduce a novel flexible approach to spatiotemporal exploration of rectilinear scalar volumes. Our out‐of‐core representation, based on per‐frame levels of hierarchically tiled non‐redundant 3D grids, efficiently supports spatiotemporal random access and streaming to the GPU in compressed formats. A novel low‐bitrate codec able to store into fixed‐size pages a variable‐rate approximation based on sparse coding with learned dictionaries is exploited to meet stringent bandwidth constraint during time‐critical operations, while a near‐lossless representation is employed to support high‐quality static frame rendering. A flexible high‐speed GPU decoder and raycasting framework mixes and matches GPU kernels performing parallel object‐space and image‐space operations for seamless support, on fat and thin clients, of different exploration use cases, including animation and temporal browsing, dynamic exploration of single frames, and high‐quality snapshots generated from near‐lossless data. The quality and performance of our approach are demonstrated on large data sets with thousands of multi‐billion‐voxel frames.

Fast Neural Representations for Direct Volume Rendering

“…TThresh, for the sake of efficient GPU decoding. For example, Marton et al [MAG19] present a rendering pipeline capable of decompressing over 10 Gvoxels/s while reporting a compression ratio of 1:64 (0.5 bits per sample on floating point data). In our work we target the high compression rates achieved by offline schemes like TThresh, while still being able to render images of large volumes from the compressed representation within a second.…”