Continual Learning in 3D Point Clouds: Employing Spectral Techniques for Exemplar Selection

Hossein Resani, Behrooz Nasihatkon, Mohammadreza Alimoradi Jazi; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 2921-2931

We introduce a novel framework for Continual learning in 3D object classification. Our approach is based on the selection of prototypes from each class using spectral clustering. For non-Euclidean data such as point clouds spectral clustering can be employed as long as one can define a distance measure between pairs of samples. Choosing the appropriate distance measure enables us to leverage 3D geometric characteristics to identify representative prototypes for each class. We explore the effectiveness of clustering in the input space (3D points) local feature space (1024-dimensional points) and global feature space. We conduct experiments on the ModelNet40 ShapeNet and ScanNet datasets achieving state-of-the-art accuracy exclusively through the use of input space features. By leveraging the combined input local and global features we have improved the state-of-the-art on ModelNet40 and ShapeNet utilizing nearly half the memory used by competing approaches. For the challenging ScanNet dataset our method enhances accuracy by 4.1% while consuming just 28% of the memory used by our competitors demonstrating the scalability of our approach.