The paper was a good read as it contained consolidated algorithms present in volume visualizations. It discusses about surface fitting and direct volume rendering methods. and the importance and difficulties of data classification. Data classification in 2D itself is difficult so we can imagine how difficult it would be in 3D. When they say color and opacity are one of the major factors in classification I guess the way people perceive things also be taken into consideration.
As the paper has mentioned this paper was not able to conclude problems of handling non cartesian girds, multivariate and higher dimensional data. A better classification algorithm also should be figured out. As human minds find it difficult to categorize the volumes just by seeing it , it becomes challenging to visualize the data using some animations and easy to use interfaces. So further study and detailed expiation of these basic points like data acquisition, slicing , data set reconstruction and data classification would improvise volume visualizations in future.
As the paper has mentioned this paper was not able to conclude problems of handling non cartesian girds, multivariate and higher dimensional data. A better classification algorithm also should be figured out. As human minds find it difficult to categorize the volumes just by seeing it , it becomes challenging to visualize the data using some animations and easy to use interfaces. So further study and detailed expiation of these basic points like data acquisition, slicing , data set reconstruction and data classification would improvise volume visualizations in future.
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