The question of what are good views of a 3D object has been addressed
by numerous researchers in perception, computer vision and computer
graphics. This has led to a large variety of measures for the goodness
of views as well as some special-case viewpoint selection algorithms. In
this paper, we leverage the results of a large user study to optimize the
parameters of a general model for viewpoint goodness, such that the fitted
model can predict people’s preferred views for a broad range of objects.
Our model is represented as a combination of attributes known to be
important for view selection, such as projected model area and silhouette
length. Moreover, this framework can easily incorporate new attributes in
the future, based on the data from our existing study. We demonstrate
our combined goodness measure in a number of applications, such as
automatically selecting a good set of representative views, optimizing
camera orbits to pass through good views and avoid bad views, and trackball
controls that gently guide the viewer towards better views.