  Simultaneous object recognition and pose estimation are two key
functionalities for robots to safely interact with humans as well as
environments. Although both object recognition and pose estimation use visual
input, most state-of-the-art tackles them as two separate problems since the
former needs a view-invariant representation while object pose estimation
necessitates a view-dependent description. Nowadays, multi-view Convolutional
Neural Network (MVCNN) approaches show state-of-the-art classification
performance. Although MVCNN object recognition has been widely explored, there
has been very little research on multi-view object pose estimation methods, and
even less on addressing these two problems simultaneously. The pose of virtual
cameras in MVCNN methods is often pre-defined in advance, leading to bound the
application of such approaches. In this paper, we propose an approach capable
of handling object recognition and pose estimation simultaneously. In
particular, we develop a deep object-agnostic entropy estimation model, capable
of predicting the best viewpoints of a given 3D object. The obtained views of
the object are then fed to the network to simultaneously predict the pose and
category label of the target object. Experimental results showed that the views
obtained from such positions are descriptive enough to achieve a good accuracy
score. Code is available online at: https://github.com/tparisotto/more_mvcnn
