Knowledge distillation-based point cloud registration method

Linjun Jiang; Yinghao Li; Yue Liu; Zhiyuan Dong; Mengyuan Yao; Yusong Lin

doi:10.1117/12.3026745

27 March 2024 Knowledge distillation-based point cloud registration method

Linjun Jiang, Yinghao Li, Yue Liu, Zhiyuan Dong, Mengyuan Yao, Yusong Lin

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 131052L (2024) https://doi.org/10.1117/12.3026745
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Linjun Jiang, Yinghao Li, Yue Liu, Zhiyuan Dong, Mengyuan Yao, and Yusong Lin "Knowledge distillation-based point cloud registration method", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 131052L (27 March 2024); https://doi.org/10.1117/12.3026745

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