  Generative 3D part assembly involves understanding part relationships and
predicting their 6-DoF poses for assembling a realistic 3D shape. Prior work
often focus on the geometry of individual parts, neglecting part-whole
hierarchies of objects. Leveraging two key observations: 1) super-part poses
provide strong hints about part poses, and 2) predicting super-part poses is
easier due to fewer superparts, we propose a part-whole-hierarchy message
passing network for efficient 3D part assembly. We first introduce super-parts
by grouping geometrically similar parts without any semantic labels. Then we
employ a part-whole hierarchical encoder, wherein a super-part encoder predicts
latent super-part poses based on input parts. Subsequently, we transform the
point cloud using the latent poses, feeding it to the part encoder for
aggregating super-part information and reasoning about part relationships to
predict all part poses. In training, only ground-truth part poses are required.
During inference, the predicted latent poses of super-parts enhance
interpretability. Experimental results on the PartNet dataset show that our
method achieves state-of-the-art performance in part and connectivity accuracy
and enables an interpretable hierarchical part assembly. Code is available at
https://github.com/pkudba/3DHPA.
