基于单一部分点云多视图增强的自监督点云补全
Self-Supervised Point Cloud Completion based on Multi-View Augmentations of Single Partial Point Cloud
AI总结：
本文针对点云补全中监督依赖真值、无监督需完整点云等问题，提出基于单一部分点云多视图增强的自监督方法，并首次引入Mamba提升生成质量，在合成与真实数据集上取得最优结果。
摘要：
点云补全旨在从部分观测中重建完整形状。尽管当前方法已取得显著性能，但仍存在一些局限：监督方法严重依赖真值，由于合成到真实的域差距，限制了其对真实数据集的泛化能力；无监督方法需要完整点云来组成非配对训练数据；弱监督方法需要物体的多视图观测；现有自监督方法由于自监督信号能力有限，常常产生不令人满意的预测。为克服这些挑战，我们提出一种新颖的自监督点云补全方法。我们基于单一部分点云的多视图增强设计了一组新颖的自监督信号。此外，为增强模型的学习能力，我们首次将Mamba引入自监督点云补全任务，促使模型生成质量更好的点云。在合成数据集和真实数据集上的实验表明，我们的方法取得了最先进的结果。
英文摘要：
Point cloud completion aims to reconstruct complete shapes from partial observations. Although current methods have achieved remarkable performance, they still have some limitations: Supervised methods heavily rely on ground truth, which limits their generalization to real-world datasets due to the synthetic-to-real domain gap. Unsupervised methods require complete point clouds to compose unpaired training data, and weakly-supervised methods need multi-view observations of the object. Existing self-supervised methods frequently produce unsatisfactory predictions due to the limited capabilities of their self-supervised signals. To overcome these challenges, we propose a novel self-supervised point cloud completion method. We design a set of novel self-supervised signals based on multi-view augmentations of the single partial point cloud. Additionally, to enhance the model's learning ability, we first incorporate Mamba into self-supervised point cloud completion task, encouraging the model to generate point clouds with better quality. Experiments on synthetic and real-world datasets demonstrate that our method achieves state-of-the-art results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
