  Dense Object Nets (DONs) by Florence, Manuelli and Tedrake (2018) introduced
dense object descriptors as a novel visual object representation for the
robotics community. It is suitable for many applications including object
grasping, policy learning, etc. DONs map an RGB image depicting an object into
a descriptor space image, which implicitly encodes key features of an object
invariant to the relative camera pose. Impressively, the self-supervised
training of DONs can be applied to arbitrary objects and can be evaluated and
deployed within hours. However, the training approach relies on accurate depth
images and faces challenges with small, reflective objects, typical for
industrial settings, when using consumer grade depth cameras. In this paper we
show that given a 3D model of an object, we can generate its descriptor space
image, which allows for supervised training of DONs. We rely on Laplacian
Eigenmaps (LE) to embed the 3D model of an object into an optimally generated
space. While our approach uses more domain knowledge, it can be efficiently
applied even for smaller and reflective objects, as it does not rely on depth
information. We compare the training methods on generating 6D grasps for
industrial objects and show that our novel supervised training approach
improves the pick-and-place performance in industry-relevant tasks.
