Fast selective antialiasing for direct volume rendering

Sergey Y. Belyaev; Natalia D. Smirnova; Pavel O. Smirnov; Daniil A. Savchuk

doi:10.1117/12.2511887

15 March 2019 Fast selective antialiasing for direct volume rendering

Sergey Y. Belyaev, Natalia D. Smirnova, Pavel O. Smirnov, Daniil A. Savchuk

Proceedings Volume 10954, Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications; 1095407 (2019) https://doi.org/10.1117/12.2511887
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

One of the most popular method of 3d datasets visualization is direct volume rendering. The paper describes an algorithm which accelerates an antialiasing process in it. The new method works in two passes: the first one is executed at pixel level, the second one is done at subpixel level. In the first pass the rays are going not through the pixel centers of resulting image but with half-pixel offset. The values of volume integral for the rays including values of the volume integral over a predefined set of intervals are stored in so-called G-buffer. In the second pass if the resulting colors for adjacent rays are close, then the color of the pixel between those rays is interpolated bilinearly; otherwise several subpixels are processed for the pixel to get more accurate color value. The color values over intervals from G-buffer are used to accelerate calculation of volume integral over subpixels’ rays. The more subpixels are used the higher efficiency the approach shows. The speed also increases with growing of dataset coherence. For example, for typical medical volume data selective antialiasing with four subpixels accelerates the rendering about three times in comparison with the full-screen antialiased direct volume rendering.

Conference Presentation

Citation Download Citation

Sergey Y. Belyaev, Natalia D. Smirnova, Pavel O. Smirnov, and Daniil A. Savchuk "Fast selective antialiasing for direct volume rendering", Proc. SPIE 10954, Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications, 1095407 (15 March 2019); https://doi.org/10.1117/12.2511887

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