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<p>3D point clouds are essential for representing geometric structures in various fields such as autonomous driving and virtual reality. However, real-world data often suffers from incompleteness due to occlusions and noise, and existing completion methods typically rely on paired complete–incomplete training data or are limited to recovering relatively small missing regions, which restricts their effectiveness under high missing-rate scenarios. This paper introduces a GAN-based method for completing 3D point clouds, capable of reconstructing detailed structures from partial inputs. Our end-to-end framework, consisting of an encoder, generator, and discriminator, optimizes topological accuracy and spatial continuity through a multi-term joint loss. Experimental results on the ModelNet40 dataset demonstrate superior performance over traditional and deep learning-based methods, achieving Chamfer Distance (CD = 0.085), Earth Mover’s Distance (EMD = 0.199), and F-Score (0.208). The generated high-quality point clouds support downstream tasks like path planning and robotic grasping. The source code and experimental datasets used in this work are publicly available at: DOI: https://doi.org/10.5281/zenodo.18421141.</p>

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Generative adversarial networks for high-fidelity 3D point cloud completion

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月18日

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  • Di Zhao,
  • Sizhe Mao,
  • Junhan Shao,
  • Hui Huang

摘要

3D point clouds are essential for representing geometric structures in various fields such as autonomous driving and virtual reality. However, real-world data often suffers from incompleteness due to occlusions and noise, and existing completion methods typically rely on paired complete–incomplete training data or are limited to recovering relatively small missing regions, which restricts their effectiveness under high missing-rate scenarios. This paper introduces a GAN-based method for completing 3D point clouds, capable of reconstructing detailed structures from partial inputs. Our end-to-end framework, consisting of an encoder, generator, and discriminator, optimizes topological accuracy and spatial continuity through a multi-term joint loss. Experimental results on the ModelNet40 dataset demonstrate superior performance over traditional and deep learning-based methods, achieving Chamfer Distance (CD = 0.085), Earth Mover’s Distance (EMD = 0.199), and F-Score (0.208). The generated high-quality point clouds support downstream tasks like path planning and robotic grasping. The source code and experimental datasets used in this work are publicly available at: DOI: https://doi.org/10.5281/zenodo.18421141.