Data augmentation is a powerful technique to enhance the performance of a deep learning task but has received less attention in 3D deep learning. It is well known that when 3D shapes are sparsely represented with low point density, the performance of the downstream tasks drops significantly. This work explores test-time augmentation (TTA) for 3D point clouds. We are inspired by the recent revolution of learning implicit representation and point cloud upsampling, which can produce high-quality 3D surface reconstruction and proximity-to-surface, respectively. Our idea is to leverage the implicit field reconstruction or point cloud upsampling techniques as a systematic way to augment point cloud data. Mainly, we test both strategies by sampling points from the reconstructed results and using the sampled point cloud as test-time augmented data. We show that both strategies are effective in improving accuracy. We observed that point cloud upsampling for test-time augmentation can lead to more significant performance improvement on downstream tasks such as object classification and segmentation on the ModelNet40, ShapeNet, ScanObjectNN, and SemanticKITTI datasets, especially for sparse point clouds.

翻译：数据增强是一种提升深度学习任务性能的强大技术，但在3D深度学习领域受到的关注较少。众所周知，当3D形状以低点密度稀疏表示时，下游任务的性能会显著下降。本文探索了针对3D点云的测试时数据增强方法。受近期隐式表示学习和点云上采样领域革新的启发——前者可生成高质量3D表面重建，后者能获得近表面点云——我们提出利用隐式场重建或点云上采样技术作为系统化增强点云数据的方法。主要策略是：从重建结果中采样点云，并将这些采样点云作为测试时增强数据。实验表明两种策略均能有效提升精度。我们观察到，通过点云上采样进行测试时增强，在ModelNet40、ShapeNet、ScanObjectNN和SemanticKITTI数据集上的目标分类与分割等下游任务中，尤其对于稀疏点云，能带来更显著的性能提升。