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학술지 Reflective Noise Filtering of Large-Scale Point Cloud Using Transformer
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저자
Rui Gao, 이명우, 양승준, 조경은
발행일
202202
출처
Remote Sensing, v.14 no.3, pp.1-20
ISSN
2072-4292
출판사
MDPI
DOI
https://dx.doi.org/10.3390/rs14030577
협약과제
21ZH1200, 초실감 입체공간 미디어·콘텐츠 원천기술연구, 이태진
초록
Point clouds acquired with LiDAR are widely adopted in various fields, such as threedimensional (3D) reconstruction, autonomous driving, and robotics. However, the high-density point cloud of large scenes captured with Lidar usually contains a large number of virtual points generated by the specular reflections of reflective materials, such as glass. When applying such large-scale highdensity point clouds, reflection noise may have a significant impact on 3D reconstruction and other related techniques. In this study, we propose a method that uses deep learning and multi-position sensor comparison method to remove noise due to reflections from high-density point clouds in large scenes. The proposed method converts large-scale high-density point clouds into a range image and subsequently uses a deep learning method and multi-position sensor comparison method for noise detection. This alleviates the limitation of the deep learning networks, specifically their inability to handle large-scale high-density point clouds. The experimental results show that the proposed algorithm can effectively detect and remove noise due to reflection.
KSP 제안 키워드
3D Reconstruction, Deep learning method, Deep learning network, High-density, Large scenes, Large-scale point cloud, Noise filtering, Range Image, Reflective materials, autonomous driving, comparison method
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