Progressive CPU Volume Rendering with Sample Accumulation

Usher, Will; Amstutz, Jefferson; Brownlee, Carson; Knoll, Aaron; Wald, I.

doi:10.2312/pgv.20171090

Title: Progressive CPU Volume Rendering with Sample Accumulation

Full Record
Other Related Research

Abstract

Here, we present a new method for progressive volume rendering by accumulating object-space samples over successively rendered frames. Existing methods for progressive refinement either use image space methods or average pixels over frames, which can blur features or integrate incorrectly with respect to depth. Our approach stores samples along each ray, accumulates new samples each frame into a buffer, and progressively interleaves and integrates these samples. Though this process requires additional memory, it ensures interactivity and is well suited for CPU architectures with large memory and cache. This approach also extends well to distributed rendering in cluster environments. We implement this technique in Intel’s open source OSPRay CPU ray tracing framework and demonstrate that it is particularly useful for rendering volumetric data with costly sampling functions.

Authors:

Usher, Will ^[1]; Amstutz, Jefferson ^[2]; Brownlee, Carson ^[2]; Knoll, Aaron ^[3]; Wald, I. ^[2]

Univ. of Utah, Salt Lake City, UT (United States); Intel Corp., Mountain View, CA (United States)
Intel Corp., Mountain View, CA (United States)
Univ. of Utah, Salt Lake City, UT (United States)

Publication Date:: Sun Jan 01 00:00:00 EST 2017

Research Org.:: Univ. of Utah, Salt Lake City, UT (United States)

Sponsoring Org.:: USDOE National Nuclear Security Administration (NNSA); National Science Foundation (NSF); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR). Scientific Discovery through Advanced Computing (SciDAC); Intel Parallel Computing Centers

OSTI Identifier:: 1756054

Grant/Contract Number:: NA0002375; 1314896; SC0007446; SC0010498

Resource Type:: Accepted Manuscript

Journal Name:: Eurographics Symposium on Parallel Graphics and Visualization

Additional Journal Information:: Journal Volume: 2017; Conference: Eurographics Symposium on Parallel Graphics and Visualization (2017), Barcelona (Spain), 12-13 Jun 2017; Journal ID: ISSN 1727-348X

Publisher:: The Eurographics Association

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING; Ray Tracing, OSPRay CPU, Rendering Volumetric Data; Computer Graphics; Picture/Image Generation—Display Algorithms

Citation Formats


                    Usher, Will, Amstutz, Jefferson, Brownlee, Carson, Knoll, Aaron, and Wald, I. Progressive CPU Volume Rendering with Sample Accumulation.  United States: N. p., 2017. 
Web.  doi:10.2312/pgv.20171090.

Copy to clipboard


                    Usher, Will, Amstutz, Jefferson, Brownlee, Carson, Knoll, Aaron, & Wald, I. Progressive CPU Volume Rendering with Sample Accumulation.  United States.  https://doi.org/10.2312/pgv.20171090

Copy to clipboard


                    Usher, Will, Amstutz, Jefferson, Brownlee, Carson, Knoll, Aaron, and Wald, I. Sun .  
"Progressive CPU Volume Rendering with Sample Accumulation".  United States.  https://doi.org/10.2312/pgv.20171090.  https://www.osti.gov/servlets/purl/1756054.

Copy to clipboard


                    
@article{osti_1756054,

  title        = {Progressive CPU Volume Rendering with Sample Accumulation},

  author       = {Usher, Will and Amstutz, Jefferson and Brownlee, Carson and Knoll, Aaron and Wald, I.},

  abstractNote = {Here, we present a new method for progressive volume rendering by accumulating object-space samples over successively rendered frames. Existing methods for progressive refinement either use image space methods or average pixels over frames, which can blur features or integrate incorrectly with respect to depth. Our approach stores samples along each ray, accumulates new samples each frame into a buffer, and progressively interleaves and integrates these samples. Though this process requires additional memory, it ensures interactivity and is well suited for CPU architectures with large memory and cache. This approach also extends well to distributed rendering in cluster environments. We implement this technique in Intel’s open source OSPRay CPU ray tracing framework and demonstrate that it is particularly useful for rendering volumetric data with costly sampling functions.},

  doi          = {10.2312/pgv.20171090},

  journal      = {Eurographics Symposium on Parallel Graphics and Visualization},

  number       = ,

  volume       = 2017,

  place        = {United States},

  year         = {Sun Jan 01 00:00:00 EST 2017},

  month        = {Sun Jan 01 00:00:00 EST 2017}

}

Copy to clipboard

Journal Article:

Free Publicly Available Full Text

Accepted Manuscript (DOE)

Publisher's Version of Record

https://doi.org/10.2312/pgv.20171090

Other availability

Search WorldCat to find libraries that may hold this journal

Save / Share:

Export Metadata

Save to My Library

Similar Records in DOE PAGES and OSTI.GOV collections:

CPU Isosurface Ray Tracing of Adaptive Mesh Refinement Data

Journal Article Wang, Feng ; Wald, Ingo ; Wu, Qi ; ... - IEEE Transactions on Visualization and Computer Graphics

Adaptive mesh refinement (AMR) is a key technology for large-scale simulations that allows for adaptively changing the simulation mesh resolution, resulting in significant computational and storage savings. However, visualizing such AMR data poses a significant challenge due to the difficulties introduced by the hierarchical representation when reconstructing continuous field values. Here, we detail a comprehensive solution for interactive isosurface rendering of block-structured AMR data. We contribute a novel reconstruction strategy—the octant method—which is continuous, adaptive and simple to implement. Furthermore, we present a generally applicable hybrid implicit isosurface ray-tracing method, which provides better rendering quality and performance than the built-inmore »« less
Cited by 7
https://doi.org/10.1109/TVCG.2018.2864850

Full Text Available
CPU Volume Rendering of Adaptive Mesh Refinement Data

Conference Wald, Ingo ; Brownlee, Carson ; Usher, Will ; ... - SA '17: SIGGRAPH Asia 2017 Symposium on Visualization

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. Here, we demonstrate our system on two types of block-structuredmore »« less
https://doi.org/10.1145/3139295.3139305

Full Text Available
On-the-Fly Decompression and Rendering of Multiresolution Terrain

Conference Lindstrom, P ; Cohen, J D

We present a streaming geometry compression codec for multiresolution, uniformly-gridded, triangular terrain patches that supports very fast decompression. Our method is based on linear prediction and residual coding for lossless compression of the full-resolution data. As simplified patches on coarser levels in the hierarchy already incur some data loss, we optionally allow further quantization for more lossy compression. The quantization levels are adaptive on a per-patch basis, while still permitting seamless, adaptive tessellations of the terrain. Our geometry compression on such a hierarchy achieves compression ratios of 3:1 to 12:1. Our scheme is not only suitable for fast decompression onmore »« less
https://doi.org/10.1145/1730804.1730815

Full Text Available
SU-E-J-60: Efficient Monte Carlo Dose Calculation On CPU-GPU Heterogeneous Systems

Journal Article Xiao, K ; Chen, D. Z ; Hu, X. S ; ... - Medical Physics

Purpose: It is well-known that the performance of GPU-based Monte Carlo dose calculation implementations is bounded by memory bandwidth. One major cause of this bottleneck is the random memory writing patterns in dose deposition, which leads to several memory efficiency issues on GPU such as un-coalesced writing and atomic operations. We propose a new method to alleviate such issues on CPU-GPU heterogeneous systems, which achieves overall performance improvement for Monte Carlo dose calculation. Methods: Dose deposition is to accumulate dose into the voxels of a dose volume along the trajectories of radiation rays. Our idea is to partition this proceduremore »« less
https://doi.org/10.1118/1.4888112
Multi-core and Many-core Shared-memory Parallel Raycasting Volume Rendering Optimization and Tuning

Journal Article Howison, Mark - International Journal of High Performance Computing Applications

Given the computing industry trend of increasing processing capacity by adding more cores to a chip, the focus of this work is tuning the performance of a staple visualization algorithm, raycasting volume rendering, for shared-memory parallelism on multi-core CPUs and many-core GPUs. Our approach is to vary tunable algorithmic settings, along with known algorithmic optimizations and two different memory layouts, and measure performance in terms of absolute runtime and L2 memory cache misses. Our results indicate there is a wide variation in runtime performance on all platforms, as much as 254% for the tunable parameters we test on multi-core CPUsmore »« less
Full Text Available

Similar Records