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Proceedings Article

Advanced texture filtering: a versatile framework for reconstructing multi-dimensional image data on heterogeneous architectures

[+] Author Affiliations
Stefan Zellmann, Yvonne Percan, Ulrich Lang

Univ. zu Köln (Germany)

Proc. SPIE 9397, Visualization and Data Analysis 2015, 93970C (February 8, 2015); doi:10.1117/12.2077927
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From Conference Volume 9397

  • Visualization and Data Analysis 2015
  • David L. Kao; Ming C. Hao; Mark A. Livingston; Thomas Wischgoll
  • San Francisco, California, United States | February 08, 2015

abstract

Reconstruction of 2-d image primitives or of 3-d volumetric primitives is one of the most common operations performed by the rendering components of modern visualization systems. Because this operation is often aided by GPUs, reconstruction is typically restricted to first-order interpolation. With the advent of in situ visualization, the assumption that rendering algorithms are in general executed on GPUs is however no longer adequate. We thus propose a framework that provides versatile texture filtering capabilities: up to third-order reconstruction using various types of cubic filtering and interpolation primitives; cache-optimized algorithms that integrate seamlessly with GPGPU rendering or with software rendering that was optimized for cache-friendly "Structure of Array" (SoA) access patterns; a memory management layer (MML) that gracefully hides the complexities of extra data copies necessary for memory access optimizations such as swizzling, for rendering on GPGPUs, or for reconstruction schemes that rely on pre-filtered data arrays. We prove the effectiveness of our software architecture by integrating it into and validating it using the open source direct volume rendering (DVR) software DeskVOX. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation

Stefan Zellmann ; Yvonne Percan and Ulrich Lang
" Advanced texture filtering: a versatile framework for reconstructing multi-dimensional image data on heterogeneous architectures ", Proc. SPIE 9397, Visualization and Data Analysis 2015, 93970C (February 8, 2015); doi:10.1117/12.2077927; http://dx.doi.org/10.1117/12.2077927


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