CPU Volume Rendering of Adaptive Mesh Refinement Data

Ingo Wald, Carson Brownlee, Will Usher, Aaron Knoll
In SIGGRAPH Asia 2017 Symposium on Visualization, 2017
Fig. 1: Two examples of our method (integrated within the OSPRay ray tracer): Left: 1.8GB Cosmos AMR data, rendered in ParaView. Right: a 57GB NASA Chombo simulation, rendered with ambient occlusion and shadows alongside mesh geometry.

Abstract

Adaptive Mesh Refinement (AMR) methods are widespread in scientific computing, and visualizing the resulting data with efficient and accurate rendering methods can be vital for enabling interactive data exploration. In this work, we detail a comprehensive solution for directly volume rendering block-structured (Berger-Colella) AMR data in the OSPRay interactive CPU ray tracing framework. In particular, we contribute a general method for representing and traversing AMR data using a kd-tree structure, and four different reconstruction options, one of which in particular (the basis function approach) is novel compared to existing methods. We demonstrate our system on two types of block-structured AMR data and compressed scalar field data, and show how it can be easily used in existing production-ready applications through a prototypical integration in the widely used visualization program ParaView.

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BibTeX

@inproceedings{Wald_CVAMR_2017,
author = {Wald, Ingo and Brownlee, Carson and Usher, Will and Knoll, Aaron},
title = {CPU Volume Rendering of Adaptive Mesh Refinement Data},
booktitle = {SIGGRAPH Asia 2017 Symposium on Visualization},
series = {SA '17},
year = {2017},
isbn = {978-1-4503-5411-0},
location = {Bangkok, Thailand},
pages = {9:1--9:8},
articleno = {9},
numpages = {8},
url = {http://doi.acm.org/10.1145/3139295.3139305},
doi = {10.1145/3139295.3139305},
acmid = {3139305},
publisher = {ACM},
address = {New York, NY, USA},
}