A Feature-Driven Approach to Locating Optimal Viewpoints for Volume Visualization

Abstract

The University of Tokyo 0.305 0.988 0.675 0.740 0.013 0.524 0.535 0.738 0.000 0.309 0.687 1.000 + = 0.816 0.571 0.582 0.000 0.241 0.845 1.000 0.361 0.574 0.962 0.557 0.598 Figure 1: Locating optimal viewpoints by individually estimating the visibility quality of each feature subvolume. The value under each image represents its corresponding estimate normalized to [0.0, 1.0]. ABSTRACTOptimal viewpoint selection is an important task because it considerably influences the amount of information contained in the 2D projected images of 3D objects, and thus dominates their first impressions from a psychological point of view. Although several methods have been proposed that calculate the optimal positions of viewpoints especially for 3D surface meshes, none has been done for solid objects such as volumes. This paper presents a new method of locating such optimal viewpoints when visualizing volumes using direct volume rendering. The major idea behind our method is to decompose an entire volume into a set of feature components, and then find a globally optimal viewpoint by finding a compromise between locally optimal viewpoints for the components. As the feature components, the method employs interval volumes and their combinations that characterize the topological transitions of isosurfaces according to the scalar field. Furthermore, opacity transfer functions are also utilized to assign different weights to the decomposed components so that users can emphasize features of specific interest in the volumes. Several examples of volume datasets together with their optimal positions of viewpoints are exhibited in order to demonstrate that the method can effectively guide naive users to find optimal projections of volumes.

Context-aware volume navigation

“…In contrast to our approach, LiveSync relies on 2D slice representations and does not support individual camera flights. Additionally, several more automatic and thus less relevant view point determination approaches exist [27,4,26,28]. These techniques can be used as starting point for interaction exploration.…”

Viewpoint information channel for illustrative volume rendering

“…Best view selection algorithms have been applied to computer graphics domains, such as scene understanding and virtual exploration [20,25], and volume visualization [2,4,22,26]. Shannon's information measures, such as entropy and mutual information, have been used in these fields to measure the quality of a viewpoint from which a given scene is rendered.…”

“…Shannon's information measures, such as entropy and mutual information, have been used in these fields to measure the quality of a viewpoint from which a given scene is rendered. Viewpoint entropy, first introduced in [24] for polygonal models, has been applied to volume visualization in [2,22]. In particular, Bordoloi and Shen [2] obtained the goodness of a viewpoint from the entropy of the visibility of the volume voxels.…”

“…On the other hand, minimum VE would be obtained when most of the visibility is captured from few voxels. In the volume rendering field, Bordoloi and Shen [2] and Takahashi et al [22] adapted the polygonal viewpoint entropy [24] to a volume data set. Some examples of views corresponding to the maximum and minimum values of VMI and VE are shown in Section 5 ( Figure 12).…”

An Introduction to Viewpoint Selection in Volume Visualization

“…the one reveals largest amount of information for the underlying scene. This is the most acceptable concept in the all the relevant literatures [1][2][3][4][5][6][7][8][9][10][11].…”