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.
Real-time visualization of 3D medical data on low-performance mobile and virtual reality (VR, e.g. HTC Vive) devices is non-trivial because it is necessary to render image twice per frame for each of the eyes. The algorithm presented in this paper describes an approach that allows visualizing 3D medical data in real-time without loss of quality as well as demanding less computational resources. The proposed method is a two-pass rendering algorithm, whereby the approximate texture is rendered at the first step and optimized detailed ray casting applied to the whole scene at the second step. Since the algorithm requires no preprocessing and both passes are performed on each visualization frame, the algorithm allows to dynamically change the level of rendered isosurfaces; this is one of the chief advantages of the proposed approach. The versatility of the solution allowed its implementation for medical data visualization on various platforms, i.e. HTC Vive, Web Browsers, Android devices, and iOS devices.