This paper essentially describes five of the most commonly used fundamental volume visualization algorithms and their close relatives. Descriptions of the algorithms include: what type of data they best handle, their advantages and disadvantages, a rough idea of how well they parallelize, their space and time requirements, a few possible optimizations and enhancements.
This paper answers several questions I had when I was reading the paper about "Marching cubes". In the Future work section, much was mentioned about no knowledge about visualizing volume data that is vector, multi-modal, multi-variate,higher dimensional and non-Cartesian. Also hardware implementations of DVR and SF algorithms should be readily available now. Since, the paper is written in the year 1992, I am curious what are the developments in the area of Volume visualization in these 10 years, and I ask the class to let me know if there are any set of papers I could read to understand the advancements.
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