6 citations · 11 across the 9 of their papers we have counts for
7 papers · 1 filter
DiffusionShadow: Diffusion-based Shadow Caching for Neural Volume Rendering
Kai-Chen Tung, Qi Wu, David Bauer +3
Implicit neural representations (INRs) have gained momentum in scientific visualization due to their compactness and scalability to large datasets, making them well suited for inte…
GSCache: Real-Time Radiance Caching for Volume Path Tracing using 3D Gaussian Splatting
David Bauer, Qi Wu, Hamid Gadirov +1
Real-time path tracing is rapidly becoming the standard for rendering in entertainment and professional applications. In scientific visualization, volume rendering plays a crucial…
From Cluster to Desktop: A Cache-Accelerated INR framework for Interactive Visualization of Tera-Scale Data
Daniel Zavorotny, Qi Wu, David Bauer +1
Machine learning has enabled the use of implicit neural representations (INRs) to efficiently compress and reconstruct massive scientific datasets. However, despite advances in fas…
Photon Field Networks for Dynamic Real-Time Volumetric Global Illumination
David Bauer, Qi Wu, Kwan-Liu Ma
Volume data is commonly found in many scientific disciplines, like medicine, physics, and biology. Experts rely on robust scientific visualization techniques to extract valuable in…
HyperINR: A Fast and Predictive Hypernetwork for Implicit Neural Representations via Knowledge Distillation
Qi Wu, David Bauer, Yuyang Chen +1
Implicit Neural Representations (INRs) have recently exhibited immense potential in the field of scientific visualization for both data generation and visualization tasks. However,…
FoVolNet: Fast Volume Rendering using Foveated Deep Neural Networks
David Bauer, Qi Wu, Kwan-Liu Ma
Volume data is found in many important scientific and engineering applications. Rendering this data for visualization at high quality and interactive rates for demanding applicatio…
Interactive Volume Visualization via Multi-Resolution Hash Encoding based Neural Representation
Qi Wu, David Bauer, Michael J. Doyle +1
Neural networks have shown great potential in compressing volume data for visualization. However, due to the high cost of training and inference, such volumetric neural representat…