Hardware-Accelerated Adaptive EWA Volume Splatting

• Published in: 2004 IEEE Visualization Conference pp. 67 - 74
• Main Authors: Chen, Wei, Ren, Liu, Zwicker, Matthias, Pfister, Hanspeter
• Format: Conference Proceeding
• Language: English
• Published: Washington, DC, USA IEEE Computer Society 01.01.2004; IEEE
• Series: ACM Conferences
• Subjects: Acceleration; Adaptive filters; Applied sciences; Artificial intelligence; Computational efficiency; Computer science; control theory; systems; Computing methodologies; Computing methodologies > Computer graphics; Computing methodologies > Computer graphics > Image manipulation; Computing methodologies > Computer graphics > Rendering; Direct volume rendering; EWA filter; Exact sciences and technology; Geometry; Graphics; hardware acceleration; Image quality; Image reconstruction; Kernel; Low pass filters; Pattern recognition. Digital image processing. Computational geometry; Rendering (computer graphics); volume splatting; EWA filter; Direct volume rendering; volume splatting; hardware acceleration; Weighted average; blurring image; Adaptive filtering; Image rendering; Artefact; Pointer; Adaptive method; High pass filter; Interactive system; Image quality; Gaussian process; Graphic processing unit; Lighting; Classification; Low pass filter
• ISBN: 0780387880; 9780780387881
• DOI: 10.1109/VISUAL.2004.38

We present a hardware-accelerated adaptive EWA (elliptical weighted average) volume splatting algorithm. EWA splatting combines a Gaussian reconstruction kernel with a low-pass image filter for high image quality without aliasing artifacts or excessive blurring. We introduce a novel adaptive filtering scheme to reduce the computational cost of EWA splatting. We show how this algorithm can be efficiently implemented on modern graphics processing units (GPUs). Our implementation includes interactive classification and fast lighting. To accelerate the rendering we store splat geometry and 3D volume data locally in GPU memory. We present results for several rectilinear volume datasets that demonstrate the high image quality and interactive rendering speed of our method.