  In this paper, we focus on latent modification and generation of 3D point
cloud object models with respect to their semantic parts. Different to the
existing methods which use separate networks for part generation and assembly,
we propose a single end-to-end Autoencoder model that can handle generation and
modification of both semantic parts, and global shapes. The proposed method
supports part exchange between 3D point cloud models and composition by
different parts to form new models by directly editing latent representations.
This holistic approach does not need part-based training to learn part
representations and does not introduce any extra loss besides the standard
reconstruction loss. The experiments demonstrate the robustness of the proposed
method with different object categories and varying number of points. The
method can generate new models by integration of generative models such as GANs
and VAEs and can work with unannotated point clouds by integration of a
segmentation module.
