  Many robotic tasks involving some form of 3D visual perception greatly
benefit from a complete knowledge of the working environment. However, robots
often have to tackle unstructured environments and their onboard visual sensors
can only provide incomplete information due to limited workspaces, clutter or
object self-occlusion. In recent years, deep learning architectures for shape
completion have begun taking traction as effective means of inferring a
complete 3D object representation from partial visual data. Nevertheless, most
of the existing state-of-the-art approaches provide a fixed output resolution
in the form of voxel grids, strictly related to the size of the neural network
output stage. While this is enough for some tasks, e.g. obstacle avoidance in
navigation, grasping and manipulation require finer resolutions and simply
scaling up the neural network outputs is computationally expensive. In this
paper, we address this limitation by proposing an object shape completion
method based on an implicit 3D representation providing a confidence value for
each reconstructed point. As a second contribution, we propose a gradient-based
method for efficiently sampling such implicit function at an arbitrary
resolution, tunable at inference time. We experimentally validate our approach
by comparing reconstructed shapes with ground truths, and by deploying our
shape completion algorithm in a robotic grasping pipeline. In both cases, we
compare results with a state-of-the-art shape completion approach.
