3 research outputs found
Fast Neural Representations for Direct Volume Rendering
Despite the potential of neural scene representations to effectively compress
3D scalar fields at high reconstruction quality, the computational complexity
of the training and data reconstruction step using scene representation
networks limits their use in practical applications. In this paper, we analyze
whether scene representation networks can be modified to reduce these
limitations and whether such architectures can also be used for temporal
reconstruction tasks. We propose a novel design of scene representation
networks using GPU tensor cores to integrate the reconstruction seamlessly into
on-chip raytracing kernels, and compare the quality and performance of this
network to alternative network- and non-network-based compression schemes. The
results indicate competitive quality of our design at high compression rates,
and significantly faster decoding times and lower memory consumption during
data reconstruction. We investigate how density gradients can be computed using
the network and show an extension where density, gradient and curvature are
predicted jointly. As an alternative to spatial super-resolution approaches for
time-varying fields, we propose a solution that builds upon latent-space
interpolation to enable random access reconstruction at arbitrary granularity.
We summarize our findings in the form of an assessment of the strengths and
limitations of scene representation networks \changed{for compression domain
volume rendering, and outline future research directions
Deep Hierarchical Super-Resolution for Scientific Data Reduction and Visualization
We present an approach for hierarchical super resolution (SR) using neural
networks on an octree data representation. We train a hierarchy of neural
networks, each capable of 2x upscaling in each spatial dimension between two
levels of detail, and use these networks in tandem to facilitate large scale
factor super resolution, scaling with the number of trained networks. We
utilize these networks in a hierarchical super resolution algorithm that
upscales multiresolution data to a uniform high resolution without introducing
seam artifacts on octree node boundaries. We evaluate application of this
algorithm in a data reduction framework by dynamically downscaling input data
to an octree-based data structure to represent the multiresolution data before
compressing for additional storage reduction. We demonstrate that our approach
avoids seam artifacts common to multiresolution data formats, and show how
neural network super resolution assisted data reduction can preserve global
features better than compressors alone at the same compression ratios