Knowledge distillation-based point cloud registration method

Point cloud registration involves aligning two point clouds with different spatial perspectives, commonly used in computer vision and artificial intelligence. Traditional methods lack robustness and accuracy, while deep learning approaches require many parameters, making them computationally expensive and time-consuming. This study aims to develop a compact and efficient model using knowledge distillation techniques to address challenges like initial pose differences and incomplete overlap, improving registration accuracy. Experiments on the ModelNet40 dataset with noisy and partially overlapping point clouds show that the distilled small model achieves favorable registration outcomes with fewer parameters and training time, effectively addressing the overlap problem.