  An understanding of the nature of objects could help robots to solve both
high-level abstract tasks and improve performance at lower-level concrete
tasks. Although deep learning has facilitated progress in image understanding,
a robot's performance in problems like object recognition often depends on the
angle from which the object is observed. Traditionally, robot sorting tasks
rely on a fixed top-down view of an object. By changing its viewing angle, a
robot can select a more semantically informative view leading to better
performance for object recognition. In this paper, we introduce the problem of
semantic view selection, which seeks to find good camera poses to gain semantic
knowledge about an observed object. We propose a conceptual formulation of the
problem, together with a solvable relaxation based on clustering. We then
present a new image dataset consisting of around 10k images representing
various views of 144 objects under different poses. Finally we use this dataset
to propose a first solution to the problem by training a neural network to
predict a "semantic score" from a top view image and camera pose. The views
predicted to have higher scores are then shown to provide better clustering
results than fixed top-down views.
