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Journal Article
Reflective Noise Filtering of Large-Scale Point Cloud Using Transformer
- Authors
- Rui Gao, Mengyu Li, Seung-Jun Yang, Kyungeun Cho
- Issue Date
- 2022-02
- Citation
- Remote Sensing, v.14, no.3, pp.1-20
- ISSN
- 2072-4292
- Publisher
- MDPI
- Language
- English
- Type
- Journal Article
- Abstract
- 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 Keywords
- 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
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
