  Depth cameras are a prominent perception system for robotics, especially when
operating in natural unstructured environments. Industrial applications,
however, typically involve reflective objects under harsh lighting conditions,
a challenging scenario for depth cameras, as it induces numerous reflections
and deflections, leading to loss of robustness and deteriorated accuracy. Here,
we developed a deep model to correct the depth channel in RGBD images, aiming
to restore the depth information to the required accuracy. To train the model,
we created a novel industrial dataset that we now present to the public. The
data was collected with low-end depth cameras and the ground truth depth was
generated by multi-view fusion.
