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Article

Reflective Noise Filtering of Large-Scale Point Cloud Using Multi-Position LiDAR Sensing Data

1
Department of Multimedia Engineering, Dongguk University-Seoul, 30, Pildongro-1-gil, Jung-gu, Seoul 04620, Korea
2
Electronics and Telecommunications Research Institute, 218 Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Ali Khenchaf
Remote Sens. 2021, 13(16), 3058; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163058
Received: 21 June 2021 / Revised: 28 July 2021 / Accepted: 31 July 2021 / Published: 4 August 2021
Signals, such as point clouds captured by light detection and ranging sensors, are often affected by highly reflective objects, including specular opaque and transparent materials, such as glass, mirrors, and polished metal, which produce reflection artifacts, thereby degrading the performance of associated computer vision techniques. In traditional noise filtering methods for point clouds, noise is detected by considering the distribution of the neighboring points. However, noise generated by reflected areas is quite dense and cannot be removed by considering the point distribution. Therefore, this paper proposes a noise removal method to detect dense noise points caused by reflected objects using multi-position sensing data comparison. The proposed method is divided into three steps. First, the point cloud data are converted to range images of depth and reflective intensity. Second, the reflected area is detected using a sliding window on two converted range images. Finally, noise is filtered by comparing it with the neighbor sensor data between the detected reflected areas. Experiment results demonstrate that, unlike conventional methods, the proposed method can better filter dense and large-scale noise caused by reflective objects. In future work, we will attempt to add the RGB image to improve the accuracy of noise detection. View Full-Text
Keywords: LiDAR; point cloud denoising; noise filtering; virtual point removal; glass reflection; large-scale 3-D point cloud LiDAR; point cloud denoising; noise filtering; virtual point removal; glass reflection; large-scale 3-D point cloud
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MDPI and ACS Style

Gao, R.; Park, J.; Hu, X.; Yang, S.; Cho, K. Reflective Noise Filtering of Large-Scale Point Cloud Using Multi-Position LiDAR Sensing Data. Remote Sens. 2021, 13, 3058. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163058

AMA Style

Gao R, Park J, Hu X, Yang S, Cho K. Reflective Noise Filtering of Large-Scale Point Cloud Using Multi-Position LiDAR Sensing Data. Remote Sensing. 2021; 13(16):3058. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163058

Chicago/Turabian Style

Gao, Rui, Jisun Park, Xiaohang Hu, Seungjun Yang, and Kyungeun Cho. 2021. "Reflective Noise Filtering of Large-Scale Point Cloud Using Multi-Position LiDAR Sensing Data" Remote Sensing 13, no. 16: 3058. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163058

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