.png){kind=icon}
.png){kind=icon}
Return
TT4D: Tensor-Train-based 4D Time-Dependent Volume Rendering
.png){kind=icon}
OA
AI
DOI：10.1111/cgf.70471.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Visualizing large-scale, time-varying volumetric data using direct volume rendering remains a significant challenge in scientific visualization, particularly as data sizes and resolutions continue to increase. One promising direction for addressing this challenge is the use of tensor decompositions, which have proven to be a useful tool for the compact representation of large, high-dimensional data. However, their integration into time-dependent interactive visualization pipelines remains limited. We introduce TT4D, a memory- and time-efficient lossy compression and decompression technique for four-dimensional (4D) time-varying volume data based on the tensor train (TT) decomposition. Our approach employs efficient subsampling during decomposition to reduce memory consumption and computational cost, enabling the processing of large datasets while avoiding unnecessarily large intermediate matrices and tensors. Beyond compression, TT4D provides a GPU-based, on-the-fly decoding scheme that avoids reconstructing the entire 4D volume, supporting interactive visualization. At equivalent error levels, TT4D outperforms other transform-based compressors at random-access decompression across datasets up to 64GB. Exploiting the structure of TT cores, our approach enables adaptive multiresolution rendering, fast spatio-temporal data exploration, and interactive filtering. Together, these capabilities make TT4D a scalable solution for interactive visualization of high-resolution, time-varying volume data.
Keywords:
volume visualization
tensor approximation
compression domain volume rendering
time-varying volume data
interactive visualization
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
2.9
Papers:
496
Citations:
1.1W
