3D Object Part Segmentation with Self-supervised Learning

This project centered on validating the effectiveness of self-supervised learning in the realm of 3D object part segmentation, particularly under conditions of limited label availability. The key achievement was attaining state-of-the-art results, demonstrating that self-supervised learning methods can effectively handle tasks that traditionally rely heavily on labeled data. By reducing the dependency on extensive labeled datasets, this approach represents a significant advancement in the field of 3D object part segmentation. The project’s success indicates a potential paradigm shift in how such tasks are approached, offering a more efficient and scalable method for dealing with complex 3D segmentation challenges.