Abstract
We present a framework for high quality splatting based on elliptical Gaussian kernels. To avoid aliasing artifacts, we introduce the concept of a resampling filter, combining a reconstruction kernel with a low-pass filter. Because of the similarity to Heckbert's (1989) EWA (elliptical weighted average) filter for texture mapping, we call our technique EWA splatting. Our framework allows us to derive EWA splat primitives for volume data and for point-sampled surface data. It provides high image quality without aliasing artifacts or excessive blurring for volume data and, additionally, features anisotropic texture filtering for point-sampled surfaces. It also handles nonspherical volume kernels efficiently; hence, it is suitable for regular, rectilinear, and irregular volume datasets. Moreover, our framework introduces a novel approach to compute the footprint function, facilitating efficient perspective projection of arbitrary elliptical kernels at very little additional cost. Finally, we show that EWA volume reconstruction kernels can be reduced to surface reconstruction kernels. This makes our splat primitive universal in rendering surface and volume data.
Highlights
Volume rendering is an important technique in visualizing acquired and simulated data sets in scientific and engineering applications
Circular footprint functions, this is basically equivalent to the elliptical weighted average (EWA) resampling filter
We present a new splat primitive for volume rendering, called the EWA volume resampling filter
Summary
Volume rendering is an important technique in visualizing acquired and simulated data sets in scientific and engineering applications. We introduce a new footprint function for volume splatting algorithms integrating an elliptical Gaussian reconstruction kernel and a low-pass filter. EWA volume rendering is attractive because it prevents aliasing artifacts in the output image while avoiding excessive blurring It works with arbitrary elliptical Gaussian reconstruction kernels and efficiently supports perspective projection. Our method is based on a novel framework to compute the footprint function, which relies on the transformation of the volume data to so-called ray space. This transformation is equivalent to perspective projection. Our derivation is based on the local affine transformation of the volume data such that the reconstruction kernels can be integrated analytically.
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