Denoising Point Clouds with Intensity and Spatial Features in Rainy Weather

• Published in: 2023 IEEE International Conference on Image Processing (ICIP) pp. 3015 - 3019
• Main Authors: Han, Haozheng, Jin, Xin, Li, Zhiheng
• Format: Conference Proceeding
• Language: English
• Published: IEEE 08.10.2023
• Subjects: autonomous vehicles; Filtering; Laser radar; Noise reduction; Point cloud compression; point cloud denoising; Rain; rain noise removal; Scattering; target detection; Three-dimensional displays
• DOI: 10.1109/ICIP49359.2023.10223164

LiDAR is important for 3D vision in autonomous vehicles, but rain causes inaccurate LiDAR point clouds due to reflection and scattering. Rain noise removal without loss of environmental features becomes an inevitable challenge. This paper presents a novel point cloud denoising method with intensity and spatial features to solve the problem. It utilizes a weighted edge-preserving filter to recover distorted contours and intensities of point clouds due to the reflection of the surface attached by raindrops. A low-intensity filtering method is also proposed to remove low-intensity noise due to the reflection of rainfall. In addition, a semi-synthetic rainy point cloud dataset with point-wise annotations is created, which benefits the research on improving LiDAR perception in adverse weather. Our method outperforms existing methods in terms of precision when it achieves a high recall of 99.28%. Using denoised data by our method can improve target detection accuracy by 5.37%. It is also faster than the state-of-the-art methods and shows the potential for use in snowy weather, making it suitable for all-weather LiDAR applications.