Point clouds are often sparse and incomplete. Existing shape completion methods are incapable of generating details of objects or learning the complex point distributions. To this end, we propose a cascaded refinement network together with a coarse-to-fine strategy to synthesize the detailed object shapes. Considering the local details of partial input with the global shape information together, we can preserve the existing details in the incomplete point set and generate the missing parts with high fidelity. We also design a patch discriminator that guarantees every local area has the same pattern with the ground truth to learn the complicated point distribution. Quantitative and qualitative experiments on different datasets show that our method achieves superior results compared to existing state-of-the-art approaches on the 3D point cloud completion task. Our source code is available at https://github.com/xiaogangw/cascaded-point-completion.git.

中文翻译：

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用于点云完成的级联细化网络

点云通常稀疏且不完整。现有的形状补全方法无法生成对象的细节或学习复杂的点分布。为此，我们提出了一个级联细化网络以及一个由粗到细的策略来合成详细的对象形状。将局部输入的局部细节与全局形状信息一起考虑，我们可以保留不完整点集中现有的细节，并生成高保真缺失的部分。我们还设计了一个补丁鉴别器，保证每个局部区域都具有与地面实况相同的模式，以学习复杂的点分布。对不同数据集的定量和定性实验表明，与现有的最先进的 3D 点云完成任务方法相比，我们的方法取得了更好的结果。我们的源代码可在 https://github.com/xiaogangw/cascaded-point-completion.git 获得。