GPU Accelerated Visualization of Scattered Point Data (IEEE)

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As datasets continue to grow in size, visualization has become a vitally important tool for extracting meaningful knowledge. Scattered point data, which are unordered sets of point coordinates with associated measured values, arise in many contexts, such as scientific experiments, sensor networks and numerical simulations. In this paper, we present a method for visualizing such scattered point datasets. Our method is based on volume ray casting, but differs by operating directly on the unstructured samples, rather than resampling them to form voxels. We estimate the intensity of the volume at points along the rays by interpolation using nearby samples, taking advantage of an octree to facilitate efficient range search. The method has been implemented both for multi-core CPUs, GPUs and multi-GPU systems1. To test our method, actual X-ray diffraction datasets have been used, consisting of up to 240 million data points.We are able to generate images of good quality and achieve interactive frame rates in favorable cases. The GPU implementation (Nvida Tesla K20) achieves speedups of 8-14 compared to the parallel CPU version (4-core, hyperthreaded Intel i7 3770K).

Paper available at IEEE.


Department of Computer and Information Science, Norwegian University of Science and Technology
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