Wang Xiaogang, Ang Marcelo H, Lee Gim Hee
IEEE Trans Pattern Anal Mach Intell. 2022 Nov;44(11):8139-8150. doi: 10.1109/TPAMI.2021.3108410. Epub 2022 Oct 4.
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.
点云通常是稀疏且不完整的，这给实际应用带来了困难。现有的形状补全方法往往会生成缺乏细粒度细节的粗糙形状。考虑到这一点，我们引入了一种用于形状补全的双分支网络。第一个分支是一个级联形状补全子网络，用于合成完整的物体，在此我们提出将部分输入与粗糙输出一起使用，以便在密集点重建过程中保留物体细节。第二个分支是一个自动编码器，用于重建原始的部分输入。这两个分支共享一个相同的特征提取器，以学习用于形状补全的精确全局特征。此外，我们提出了两种策略，以便在没有真实数据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真实训练数据的依赖，而这些数据在实际应用中往往很难获得。此外，我们提出的策略还能够提高全监督学习的重建质量。我们在自监督、半监督和全监督设置下验证了我们的方法，取得了卓越的性能。在不同数据集上的定量和定性结果表明，在点云补全任务中，我们的方法比现有方法能实现更逼真的输出。
