Generate What You Canxe2x80x99t See  a Viewdependent Image Generation 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 timeconsuming. 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 CNNbased 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.

Visual Completion Of 3D Object Shapes From A Single View For Robotic Tasks The goal of this paper is to predict 3D object shape to improve the visual perception of robots in grasping and manipulation tasks. The planning of imagebased robotic manipulation tasks depends on the recognition of the objectxe2x80x99s shape. Mostly, the manipulator robots usually use a camera with configuration eyeinhand. This fact limits the calculation of the grip on the visible part of the object. In this paper, we present a 3D Deep Convolutional Neural Network to predict the hidden parts of objects from a singleview and to accomplish recovering the complete shape of them. We have tested our proposal with both previously seen objects and novel objects from a wellknown dataset.