Generative 3D Part Assembly via Part-Whole-Hierarchy Message Passing

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 super-parts 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 that our method achieves state-of-the-art performance in part and connectivity accuracy and enables an interpretable hierarchical part assembly.