Iterative Displacement for Point Cloud Denoising

Adverse weather conditions significantly impact the reliability of perception systems in autonomous driving. To address this issue, we designed an iterative point cloud denoising method that iteratively adds displacement to help the point cloud converge to the ground truth. We constructed a foggy weather dataset based on KITTI to simulate foggy driving environments. Experimental results demonstrate that our method outperforms state-of-the-art methods and significantly improves the quality of denoised point clouds. Furthermore, we used our method as a preprocessing step for multiple point cloud registration modules, and the results showed that our method exhibits good generalization across different registration algorithms. It is well-suited as a preprocessing module embedded in downstream point cloud processing tasks.