Hybrid Visualization of Sparse Point-Based Data Using GPGPU

Direct point-based rendering is a popular method in scientific visualization, since the number of point-based datasets increased dramatically in the past few years. At the same time, rendering of point primitives is becoming less efficient as the data size increases. Point splatting, volume-based re...

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Bibliographic Details
Published in2014 Second International Symposium on Computing and Networking pp. 178 - 184
Main Authors Lukac, Niko, Pelic, Denis, Alik, Borut
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2014
Subjects
Casting
CUDA
Data visualization
GPGPU
Graphics processing units
hybrid rendering
Pipelines
Rendering (computer graphics)
scientific visualization
SVO
Three-dimensional displays
Visualization
Online AccessGet full text
ISSN2379-1888
DOI10.1109/CANDAR.2014.76

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Summary:Direct point-based rendering is a popular method in scientific visualization, since the number of point-based datasets increased dramatically in the past few years. At the same time, rendering of point primitives is becoming less efficient as the data size increases. Point splatting, volume-based rendering, or is surface extraction are well-known approaches that can be utilized. Unfortunately, high visual accuracy is often sacrificed. Furthermore, unstructured sparse point-based data is more difficult to visualize, since no surface geometry and topology can be implicitly defined. This paper introduces a novel hybrid visualization method that utilizes point-based and volume-based rendering of sparse point-based data. The visualization is done entirely with a custom rendering pipeline by using GPGPU, which provides accelerated rendering. The method achieves real-time visualization, while retaining high visual accuracy, as shown on cosmological dark matter SPH-based dataset.
ISSN:2379-1888
DOI:10.1109/CANDAR.2014.76