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

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.

@Article{MAG19,
  author       = {Marton, F. and Agus, M. and Gobbetti, E.},
  title        = {A framework for GPU-accelerated exploration of massive time-varying rectilinear scalar volumes},
  journal      = {Computer Graphics Forum},
  number       = {3},
  volume       = {38},
  pages        = {53-66},
  year         = {2019},
  keywords     = {Panoramic scene understanding Omnidirectional images Mobile capture Indoor reconstruction As-built models},
  doi          = {10.1111/cgf.13671},
  url          = {https://publications.crs4.it/pubdocs/2019/MAG19},
}