Abstract
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.
Highlights
Splatting is a popular algorithm for direct volume rendering that was first proposed by Westover [30]
In this paper we focus on volume splatting, which offers the most flexibility in terms of volume grids and mixing with point-sampled geometry [35]
We present a distance-dependent classification criteria for adaptive EWA volume splatting based on a careful analysis of the EWA volume resampling filter (Equation 4)
Summary
Splatting is a popular algorithm for direct volume rendering that was first proposed by Westover [30]. EWA splatting is still difficult due to the computational complexity of EWA filtering and insufficient commodity hardware support These two issues limit the applicability of high quality EWA volume splatting. We present a hardware-accelerated EWA volume splatting framework that allows interactive high quality volume rendering, interactive transfer function design, and fast two-pass shading. Our approach stores both the proxy geometry (i.e., the textured quads representing the splats) and the 3D volume data locally in graphics hardware for efficient access during interactive rendering. This leads to two advantages over previous approaches.
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