  Strong evidence suggests that humans perceive the 3D world by parsing visual
scenes and objects into part-whole hierarchies. Although deep neural networks
have the capability of learning powerful multi-level representations, they can
not explicitly model part-whole hierarchies, which limits their expressiveness
and interpretability in processing 3D vision data such as point clouds. To this
end, we propose an encoder-decoder style latent variable model that explicitly
learns the part-whole hierarchies for the multi-level point cloud segmentation.
Specifically, the encoder takes a point cloud as input and predicts the
per-point latent subpart distribution at the middle level. The decoder takes
the latent variable and the feature from the encoder as an input and predicts
the per-point part distribution at the top level. During training, only
annotated part labels at the top level are provided, thus making the whole
framework weakly supervised. We explore two kinds of approximated inference
algorithms, i.e., most-probable-latent and Monte Carlo methods, and three
stochastic gradient estimations for learning discrete latent variables, i.e.,
straight-through, REINFORCE, and pathwise estimators. Experimental results on
the PartNet dataset show that the proposed method achieves state-of-the-art
performance in not only top-level part segmentation but also middle-level
latent subpart segmentation.
