Robust multi-task learning network for complex LiDAR point cloud data preprocessing

Zhao, Luda; Hu, Yihua; Yang, Xing; Dou, Zhenglei; Kang, Linshuang

doi:10.1016/j.eswa.2023.121552

Cited by 9 publications

(1 citation statement)

References 31 publications

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“…The algorithm combined the advantages of FC layers and FoldingNet. Then, Zhao et al [58] continued to employ the FoldingNet for remote sensing point cloud completion.…”

Section: B Learning-based Methodsmentioning

confidence: 99%

A Survey of Point Cloud Completion

Zhuang,

Zhi,

Han

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Point cloud completion is able to estimate the complete point cloud starting from the missing point cloud, which obtains higher quality point cloud data for widely used in remote sensing 3D modeling, medical imaging, robot vision, etc. The challenge of point clouds mainly lies in the disordered and unstructured nature, which makes point cloud completion difficult. Point cloud completion research can be broadly categorized into traditional approaches and deep learning based methods. Recently, intensive research has primarily focused on deep learning based methods, given robustness and efficiency in processing the substantial missing data encountered in complex real world scenes. In addition, deep learning based methods have higher generalization performance. To stimulate future research, this survey presents a comprehensive review of existing traditional and deep learning based 3D point cloud completion methods. This review conducts extensive examinations of each stage of the process, providing a compilation of famous datasets, metrics, and their respective characteristics. Additionally, the impacts of subsequent downstream application tasks with or without completion are discussed, followed by some potential future issues in point cloud completion.

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“…The algorithm combined the advantages of FC layers and FoldingNet. Then, Zhao et al [58] continued to employ the FoldingNet for remote sensing point cloud completion.…”

Section: B Learning-based Methodsmentioning

confidence: 99%