Progressive CPU Volume Rendering with Sample Accumulation (Unknown language)

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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.

Table of contents conference proceedings

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PaViz: A Power-Adaptive Framework for Optimizing Visualization Performance
Labasan, Stephanie / Larsen, Matthew / Childs, Hank / Rountree, Barry | 2017
Prediction of Distributed Volume Visualization Performance to Support Render Hardware Acquisition
Tkachev, Gleb / Frey, Steffen / Müller, Christoph / Bruder, Valentin / Ertl, Thomas | 2017
Progressive CPU Volume Rendering with Sample Accumulation
Usher, Will / Amstutz, Jefferson / Brownlee, Carson / Knoll, Aaron / Wald, Ingo | 2017
Photo-Guided Exploration of Volume Data Features
Raji, Mohammad / Hota, Alok / Sisneros, Robert / Messmer, Peter / Huang, Jian | 2017
A Space-Efficient Method for Navigable Ensemble Analysis and Visualization
Hota, Alok / Raji, Mohammad / Hobson, Tanner / Huang, Jian | 2017
Interactive Exploration of Dissipation Element Geometry
Vierjahn, Tom / Schnorr, Andrea / Weyers, Benjamin / Denker, Dominik / Wald, Ingo / Garth, Christoph / Kuhlen, Torsten W. / Hentschel, Bernd | 2017
A Task-Based Parallel Rendering Component For Large-Scale Visualization Applications
Biedert, Tim / Werner, Kilian / Hentschel, Bernd / Garth, Christoph | 2017
Achieving Portable Performance For Wavelet Compression Using Data Parallel Primitives
Li, Shaomeng / Marsaglia, Nicole / Chen, Vincent / Sewell, Christopher / Clyne, John / Childs, Hank | 2017