EGPGV25: Eurographics Symposium on Parallel Graphics and Visualization

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https://diglib.eg.org/handle/10.2312/3607184

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Browsing EGPGV25: Eurographics Symposium on Parallel Graphics and Visualization by Subject "Volumetric models"

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    From Cluster to Desktop: A Cache-Accelerated INR framework for Interactive Visualization of Tera-Scale Data
    (The Eurographics Association, 2025) Zavorotny, Daniel; Wu, Qi; Bauer, David; Ma, Kwan-Liu; Reina, Guido; Rizzi, Silvio; Gueunet, Charles
    Machine learning has enabled the use of implicit neural representations (INRs) to efficiently compress and reconstruct massive scientific datasets. However, despite advances in fast INR rendering algorithms, INR-based rendering remains computationally expensive, as computing data values from an INR is significantly slower than reading them from GPU memory. This bottleneck currently restricts interactive INR visualization to professional workstations. To address this challenge, we introduce an INR rendering framework accelerated by a scalable, multi-resolution GPU cache capable of efficiently representing tera-scale datasets. By minimizing redundant data queries and prioritizing novel volume regions, our method reduces the number of INR computations per frame, achieving an average 5× speedup over the state-of-the-art INR rendering method while still maintaining high visualization quality. Coupled with existing hardware-accelerated INR compressors, our framework enables scientists to generate and compress massive datasets in situ on high-performance computing platforms and then interactively explore them on consumer-grade hardware post hoc.
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    Multi-Density Woodcock Tracking: Efficient & High-Quality Rendering for Multi-Channel Volumes
    (The Eurographics Association, 2025) Sahistan, Alper; Zellmann, Stefan; Morrical, Nate; Pascucci, Valerio; Wald, Ingo; Reina, Guido; Rizzi, Silvio; Gueunet, Charles
    Volume rendering techniques for scientific visualization have increasingly transitioned toward Monte Carlo (MC) methods in recent years due to their flexibility and robustness. However, their application in multi-channel visualization remains underexplored. Traditional compositing-based approaches often employ arbitrary color blending functions, which lack a physical basis and can obscure data interpretation. We introduce multi-density Woodcock tracking, a simple and flexible extension of Woodcock tracking for multi-channel volume rendering that leverages the strengths of Monte Carlo methods to generate high-fidelity visuals. Our method offers a physically grounded solution for inter-channel color blending and eliminates the need for arbitrary blending functions. We also propose a unified blending modality by generalizing Woodcock's distance tracking method, facilitating seamless integration of alternative blending functions from prior works. Through evaluation across diverse datasets, we demonstrate that our approach maintains real-time interactivity while achieving high-quality visuals by accumulating frames over time.