  In deformable object manipulation, we often want to interact with specific
segments of an object that are only defined in non-deformed models of the
object. We thus require a system that can recognize and locate these segments
in sensor data of deformed real world objects. This is normally done using
deformable object registration, which is problem specific and complex to tune.
Recent methods utilize neural occupancy functions to improve deformable object
registration by registering to an object reconstruction. Going one step
further, we propose a system that in addition to reconstruction learns
segmentation of the reconstructed object. As the resulting output already
contains the information about the segments, we can skip the registration
process. Tested on a variety of deformable objects in simulation and the real
world, we demonstrate that our method learns to robustly find these segments.
We also introduce a simple sampling algorithm to generate better training data
for occupancy learning.
