6 citations · 13 across the 11 of their papers we have counts for
11 papers
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…
Glyph-Based Multiscale Visualization of Turbulent Multi-Physics Statistics
Arisa Cowe, Tyson Neuroth, Qi Wu +4
Many scientific and engineering problems involving multi-physics span a wide range of scales. Understanding the interactions across these scales is essential for fully comprehendin…
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…
HyperFLINT: Hypernetwork-based Flow Estimation and Temporal Interpolation for Scientific Ensemble Visualization
Hamid Gadirov, Qi Wu, David Bauer +3
We present HyperFLINT (Hypernetwork-based FLow estimation and temporal INTerpolation), a novel deep learning-based approach for estimating flow fields, temporally interpolating sca…
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,…
Distributed Neural Representation for Reactive in situ Visualization
Qi Wu, Joseph A. Insley, Victor A. Mateevitsi +3
Implicit neural representations (INRs) have emerged as a powerful tool for compressing large-scale volume data. This opens up new possibilities for in situ visualization. However,…
Beyond ExaBricks: GPU Volume Path Tracing of AMR Data
Stefan Zellmann, Qi Wu, Alper Sahistan +2
Adaptive Mesh Refinement (AMR) is becoming a prevalent data representation for scientific visualization. Resulting from large fluid mechanics simulations, the data is usually cell…
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…
Diva: A Declarative and Reactive Language for In-Situ Visualization
Qi Wu, Tyson Neuroth, Oleg Igouchkine +3
The use of adaptive workflow management for in situ visualization and analysis has been a growing trend in large-scale scientific simulations. However, coordinating adaptive workfl…