Three-dimensional point cloud denoising

doi:10.7523/j.ucas.2022.038

Abstract

Abstract: With the development of 3D data acquisition technology, point cloud wins the favor of researchers for it's simple but effective representation and it is widely used in the fields of remote sensing, scene reconstruction, 3D modeling, etc. Considering that the data acquisition process is easily disturbed by many factors such as equipment, environment and material, raw point cloud is often corrupted with noise and so it is of great significance to explore robust and efficient denoising algorithms. This paper firstly investigates the relevant research works of point cloud denoising and divides them into traditional algorithms based on the optimization idea and denoising algorithms based on the deep learning idea according to the implementation principles. Secondly, the research progress of each kind of algorithm is discussed and a detailed analysis of representative algorithms is presented. Thirdly, the data sets, the evaluation metrics and experimental results are summarized with an in-depth comparison. Finally, the problems and possible development directions and trends of point cloud denoising are prospected.

Key words: three-dimensional point cloud, denoising, optimization strategy, deep learning

CLC Number:

TP391

XIAO Jun, SHI Guangtian. Three-dimensional point cloud denoising[J]. Journal of University of Chinese Academy of Sciences, 2023, 40(5): 577-595.

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