CVPR 2023 - Self-Supervised Learning for Multimodal Non-Rigid 3D Shape Matching

In this episode we discuss Self-Supervised Learning for Multimodal Non-Rigid 3D Shape Matching by Dongliang Cao, Florian Bernard. The paper proposes a self-supervised multimodal learning strategy to bridge the gap between mesh-based and point cloud-based shape matching methods. Meshes provide rich topological information but require curation, while point clouds are commonly used for real-world data but lack the same matching quality. The proposed approach combines mesh-based functional map regularization with a contrastive loss that links mesh and point cloud data. Results show that the method achieves state-of-the-art performance on benchmark datasets and exhibits cross-dataset generalization ability. Code is available for use.