Computation of good viewpoints is important in several fields:
computational geometry, visual servoing, robot motion, graph
drawing, etc. In addition, selection of good views is rapidly
becoming a key issue in computer graphics due to the new techniques
of Image Based Rendering. Although there is no consensus about what
a good view means in Computer Graphics, the quality of a viewpoint
is intuitively related to how much information it gives us about a
scene. In this paper we use the theoretical basis provided by
Information Theory to define a new measure, viewpoint entropy, that
allows us to compute good viewing positions automatically. We also
show how it can be used to select a set of N good views of a scene
for scene understanding. Finally, we design an algorithm that uses
this measure to explore automatically objects or scenes.
