Mesh Oversegmentation with Segmentation-Aware Loss

Superfacets are generated by clustering adjacent mesh faces that share similar characteristics, which can serve as processing units in downstream mesh applications. While there are existing deep neural networks that generate superpixels and superpoints/supervoxels from images and point clouds respectively, the current oversegmentation methods in 3D meshes mostly rely on hand-crafted features that are extracted using non-differentiable algorithms to generate superfacets. Nevertheless, these methods cannot leverage the feature extraction abilities of deep neural networks to generate superfacets in an end-to-end fashion. Therefore, we propose an end-to-end trainable deep neural network that learns to generate boundary-aware superfacets from 3D meshes. Specifically, our network learns a soft face-superfacet association map from faces and their adjacency relationships. Moreover, we develop a segmentation-aware loss on the faces that train the network to predict their labels in an end-to-end manner. We evaluate the performance of our method using mesh adaptations of two well-known superpixel evaluation metrics where experimental results demonstrate that the performance of our proposed network surpasses that of other state-of-the-art mesh oversegmentation methods, and in doing so simultaneously improves superfacet-based semantic segmentation.