The convenience of 3D sensors has led to an increase in the use of 3D point clouds in various applications. However, the differences in acquisition devices or scenarios lead to divergence in the data distribution of point clouds, which requires good generalization of point cloud representation learning methods. While most previous methods rely on domain adaptation, which involves fine-tuning pre-trained models on target domain data, this may not always be feasible in real-world scenarios where target domain data may be unavailable. To address this issue, we propose InvariantOODG, which learns invariability between point clouds with different distributions using a two-branch network to extract local-to-global features from original and augmented point clouds. Specifically, to enhance local feature learning of point clouds, we define a set of learnable anchor points that locate the most useful local regions and two types of transformations to augment the input point clouds. The experimental results demonstrate the effectiveness of the proposed model on 3D domain generalization benchmarks.

翻译：3D传感器的便捷性推动了其在各类应用中使用的增长。然而，采集设备或场景的差异导致点云数据分布存在分歧，这就要求点云表示学习方法具有良好的泛化能力。尽管此前大多数方法依赖领域自适应（即在目标域数据上微调预训练模型），但在目标域数据可能无法获取的真实场景中，这一做法未必可行。为解决此问题，我们提出InvariantOODG——该方法通过一个双分支网络从原始点云及其增强版本中提取局部到全局特征，从而学习不同分布点云之间的不变性。具体而言，为增强点云的局部特征学习，我们定义了一组可学习的锚点（用于定位最具价值的局部区域）以及两种用于增强输入点云的变换方式。实验结果表明，所提模型在3D领域泛化基准测试中具有有效性。