  In order to operate autonomously, a robot should explore the environment and
build a model of each of the surrounding objects. A common approach is to
carefully scan the whole workspace. This is time-consuming. It is also often
impossible to reach all the viewpoints required to acquire full knowledge about
the environment. Humans can perform shape completion of occluded objects by
relying on past experience. Therefore, we propose a method that generates
images of an object from various viewpoints using a single input RGB image. A
deep neural network is trained to imagine the object appearance from many
viewpoints. We present the whole pipeline, which takes a single RGB image as
input and returns a sequence of RGB and depth images of the object. The method
utilizes a CNN-based object detector to extract the object from the natural
scene. Then, the proposed network generates a set of RGB and depth images. We
show the results both on a synthetic dataset and on real images.
