When a robot perceives its environment, it is not only important to know what kind of objects are present in it, but also
how they relate to each other. For example in a cleanup task in a cluttered environment, a sensible
strategy is to pick the objects with the least contacts to other objects first, to minimize the chance of unwanted
movements not related to the current picking action. Estimating object contacts
in cluttered scenes only based on passive observation is a complex problem.
To tackle this problem, we present a deep neural network that learns physically stable object relations directly
from geometric features. The learned relations are encoded as contact graphs between the objects. To facilitate
training of the network, we generated a rich, publicly available dataset consisting of more than 25000 unique contact scenes,
by utilizing a physics simulation. Different deep architectures have been evaluated and the final
architecture, which shows good results in reconstructing contact graphs, is evaluated quantitatively and qualitatively.