Point cloud completion estimates complete shapes from incomplete point clouds to obtain higher-quality point cloud data. Most existing methods only consider global object features, ignoring spatial and semantic information of adjacent points. They cannot distinguish structural information well between different object parts, and the robustness of models is poor. To tackle these challenges, we propose an information interaction-based generative network for point cloud completion ($\mathbf{DualGenerator}$). It contains an adversarial generation path and a variational generation path, which interact with each other and share weights. DualGenerator introduces a local refinement module in generation paths, which captures general structures from partial inputs, and then refines shape details of the point cloud. It promotes completion in the unknown region and makes a distinction between different parts more obvious. Moreover, we design DGStyleGAN to improve the generation quality further. It promotes the robustness of this network combined with fusion analysis of dual-path completion results. Qualitative and quantitative evaluations demonstrate that our method is superior on MVP and Completion3D datasets. The performance will not degrade significantly after adding noise interference or sparse sampling.

翻译：摘要：点云补全旨在从不完整的点云中估计完整形状，从而获取更高质量的点云数据。现有方法大多仅考虑全局物体特征，而忽略了邻近点的空间与语义信息，难以有效区分不同物体部件间的结构信息，且模型鲁棒性较差。为应对这些挑战，我们提出了一种基于信息交互的生成网络用于点云补全（$\mathbf{DualGenerator}$）。该网络包含对抗生成路径和变分生成路径，二者相互交互并共享权重。DualGenerator在生成路径中引入局部细化模块，该模块从局部输入中捕获整体结构，进而细化点云的形状细节，促进未知区域的补全，并增强不同部件间的区分度。此外，我们设计了DGStyleGAN以进一步提升生成质量，该模块结合双路径补全结果的融合分析，增强了网络的鲁棒性。定性与定量评估表明，我们的方法在MVP和Completion3D数据集上表现优异，且在加入噪声干扰或稀疏采样后性能未出现显著下降。