  We propose a mechanism to reconstruct part annotated 3D point clouds of
objects given just a single input image. We demonstrate that jointly training
for both reconstruction and segmentation leads to improved performance in both
the tasks, when compared to training for each task individually. The key idea
is to propagate information from each task so as to aid the other during the
training procedure. Towards this end, we introduce a location-aware
segmentation loss in the training regime. We empirically show the effectiveness
of the proposed loss in generating more faithful part reconstructions while
also improving segmentation accuracy. We thoroughly evaluate the proposed
approach on different object categories from the ShapeNet dataset to obtain
improved results in reconstruction as well as segmentation.
