Elemental Composite Prototypical Network: Few-Shot Object Detection on Outdoor 3D Point Cloud Scenes

This paper introduces the Elemental Composite Prototypical Network (ECPN), a novel approach to few-shot learning (FSL) in outdoor 3D point cloud object detection. Such point clouds are inherently non-uniformly packed and show marked intra-class variations due to aberrations in lidar scanning methods. Due to the limited availability of examples in the FSL setting, the intra-class variations serve as a much more formidable challenge to traditional detection algorithms. ECPN employs a novel prototypical learning method that solves the issues mentioned above. We generate and leverage multiple elemental prototypes for each class to capture essential geometric features from limited examples. These elemental prototypes are then combined in a weighted manner to arrive at composite prototypes that score relevant and irrelevant features in the elemental prototypes with respect to the query point cloud scene. Moreover, we introduce a novel feature-similarity-discrimination loss to refine the model's ability to distinguish between relevant objects and their background, significantly improving object detection accuracy in FSL scenarios. Our extensive testing on the nuScenes dataset demonstrates that ECPN significantly outperforms existing baselines, offering a robust solution to the complexities of outdoor few-shot 3D object detection (O-FS3D) and setting a new standard for future research.