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3D scalar data visualization has been well accepted as a powerful means to allow scientists to explore large data sets, and to present their results to a wider audience. However, most of these visualization techniques have been designed on an assumption that the 3D scalar data to be represented are free from uncertainty. This is rarely the case in the real world, as uncertainty is pervasive in 3D scalar data and there is a growing need to depict the uncertainty in 3D scalar data visualization. Overlooking such depictions in 3D scalar data visualization could result in unfaithful representation of the 3D scalar data, and leave the visualization running in a risk that misleads viewers interpretation, conclusions or even decisions from the 3D scalar data. Therefore, the topic of this book is about the study of how to efficiently represent uncertainty that is associated to the 3D scalar data in 3D scalar data visualization. This is also known as uncertainty visualization for 3D scalar data, which is currently a hot research topic in the visualization community.

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Ji Ma received his MSc in the Department of Computer Science, and his PhD in the Department of Computer Science and Tyndall National Institute, from University College Cork (UCC), Ireland, in 2009 and 2014, respectively. He is now a lecturer in the College of Computer Science & Technology, Zhejiang University of Technology (ZJUT), Hangzhou, China.

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