Point clouds are often sparse and incomplete, which imposes difficulties for real-world applications. Existing shape completion methods tend to generate rough shapes without fine-grained details. Considering this, we introduce a two-branch network for shape completion. The first branch is a cascaded shape completion sub-network to synthesize complete objects, where we propose to use the partial input together with the coarse output to preserve the object details during the dense point reconstruction. The second branch is an auto-encoder to reconstruct the original partial input. The two branches share a same feature extractor to learn an accurate global feature for shape completion. Furthermore, we propose two strategies to enable the training of our network when ground truth data are not available. This is to mitigate the dependence of existing approaches on large amounts of ground truth training data that are often difficult to obtain in real-world applications. Additionally, our proposed strategies are also able to improve the reconstruction quality for fully supervised learning. We verify our approach in self-supervised, semi-supervised and fully supervised settings with superior performances. Quantitative and qualitative results on different datasets demonstrate that our method achieves more realistic outputs than state-of-the-art approaches on the point cloud completion task.

中文翻译：

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具有自我监督的点云完成的级联细化网络。

点云通常稀疏且不完整，这给实际应用带来了困难。现有的形状补全方法倾向于生成没有细粒度细节的粗糙形状。考虑到这一点，我们引入了一个用于形状完成的两分支网络。第一个分支是一个级联的形状补全子网络，用于合成完整的对象，我们建议在密集点重建期间使用部分输入和粗略输出来保留对象细节。第二个分支是用于重建原始部分输入的自动编码器。这两个分支共享相同的特征提取器来学习准确的全局特征以完成形状。此外，我们提出了两种策略，以在地面实况数据不可用时对我们的网络进行训练。这是为了减轻现有方法对大量地面实况训练数据的依赖，这些训练数据在实际应用中通常难以获得。此外，我们提出的策略还能够提高完全监督学习的重建质量。我们在具有卓越性能的自我监督、半监督和完全监督的环境中验证了我们的方法。不同数据集上的定量和定性结果表明，我们的方法在点云完成任务上比最先进的方法实现了更真实的输出。我们在具有卓越性能的自我监督、半监督和完全监督的环境中验证了我们的方法。不同数据集上的定量和定性结果表明，我们的方法在点云完成任务上比最先进的方法实现了更真实的输出。我们在具有卓越性能的自我监督、半监督和完全监督的环境中验证了我们的方法。不同数据集上的定量和定性结果表明，我们的方法在点云完成任务上比最先进的方法实现了更真实的输出。