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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2508.11932 |
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| _version_ | 1866915448096817152 |
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| author | Zhang, Chengwei Zhang, Xueyi Lao, Mingrui Jiang, Tao Xu, Xinhao Li, Wenjie Zhang, Fubo Chen, Longyong |
| author_facet | Zhang, Chengwei Zhang, Xueyi Lao, Mingrui Jiang, Tao Xu, Xinhao Li, Wenjie Zhang, Fubo Chen, Longyong |
| contents | Real-world environment-derived point clouds invariably exhibit noise across varying modalities and intensities. Hence, point cloud denoising (PCD) is essential as a preprocessing step to improve downstream task performance. Deep learning (DL)-based PCD models, known for their strong representation capabilities and flexible architectures, have surpassed traditional methods in denoising performance. To our best knowledge, despite recent advances in performance, no comprehensive survey systematically summarizes the developments of DL-based PCD. To fill the gap, this paper seeks to identify key challenges in DL-based PCD, summarizes the main contributions of existing methods, and proposes a taxonomy tailored to denoising tasks. To achieve this goal, we formulate PCD as a two-step process: outlier removal and surface noise restoration, encompassing most scenarios and requirements of PCD. Additionally, we compare methods in terms of similarities, differences, and respective advantages. Finally, we discuss research limitations and future directions, offering insights for further advancements in PCD. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_11932 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Learning For Point Cloud Denoising: A Survey Zhang, Chengwei Zhang, Xueyi Lao, Mingrui Jiang, Tao Xu, Xinhao Li, Wenjie Zhang, Fubo Chen, Longyong Computer Vision and Pattern Recognition Real-world environment-derived point clouds invariably exhibit noise across varying modalities and intensities. Hence, point cloud denoising (PCD) is essential as a preprocessing step to improve downstream task performance. Deep learning (DL)-based PCD models, known for their strong representation capabilities and flexible architectures, have surpassed traditional methods in denoising performance. To our best knowledge, despite recent advances in performance, no comprehensive survey systematically summarizes the developments of DL-based PCD. To fill the gap, this paper seeks to identify key challenges in DL-based PCD, summarizes the main contributions of existing methods, and proposes a taxonomy tailored to denoising tasks. To achieve this goal, we formulate PCD as a two-step process: outlier removal and surface noise restoration, encompassing most scenarios and requirements of PCD. Additionally, we compare methods in terms of similarities, differences, and respective advantages. Finally, we discuss research limitations and future directions, offering insights for further advancements in PCD. |
| title | Deep Learning For Point Cloud Denoising: A Survey |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.11932 |