3D Shape Segmentation via Attentive Nonuniform Downsampling

The segmentation of 3D shapes is a critical aspect of shape analysis. However, most existing methods for 3D shape segmentation treat each face of the original mesh model with equal importance. This uniform approach becomes problematic in areas where the faces are smaller but denser, especially around the junctions of different segments. In such regions, greater importance should be assigned compared to the flatter areas. To address this issue, this paper proposes a novel 3D shape segmentation method that incorporates attentive nonuniform sampling into the segmentation pipeline. By leveraging a transformer-based mechanism, our method adaptively identifies the intricate details of 3D shapes, calculating varying degrees of attention to each face. Consequently, the mesh model is downsampled by eliminating faces with lower attention, thereby optimizing the segmentation process. Our approach outperforms most state-of-the-art methods on multiple public datasets, making it a promising avenue for future research.