Transfer learning has long been a key factor in the advancement of many fields including 2D image analysis. Unfortunately, its applicability in 3D data processing has been relatively limited. While several approaches for 3D transfer learning have been proposed in recent literature, with contrastive learning gaining particular prominence, most existing methods in this domain have only been studied and evaluated in limited scenarios. Most importantly, there is currently a lack of principled understanding of both when and why 3D transfer learning methods are applicable. Remarkably, even the applicability of standard supervised pre-training is poorly understood. In this work, we conduct the first in-depth quantitative and qualitative investigation of supervised and contrastive pre-training strategies and their utility in downstream 3D tasks. We demonstrate that layer-wise analysis of learned features provides significant insight into the downstream utility of trained networks. Informed by this analysis, we propose a simple geometric regularization strategy, which improves the transferability of supervised pre-training. Our work thus sheds light onto both the specific challenges of 3D transfer learning, as well as strategies to overcome them.

翻译：迁移学习长期以来一直是推动包括二维图像分析在内的诸多领域进步的关键因素。然而，其在三维数据处理中的适用性相对有限。尽管近年文献中提出了多种三维迁移学习方法，其中对比学习尤为突出，但该领域现有的大多数方法仅在有限场景下得到研究和评估。最重要的是，目前缺乏对三维迁移学习方法何时以及为何适用的原则性理解。值得注意的是，即使是标准监督预训练的适用性也鲜有深入理解。在本工作中，我们首次对监督和对比预训练策略及其在下游三维任务中的效用进行了深入的定量和定性研究。我们证明，对学习特征进行逐层分析能为训练网络在下游任务中的效用提供重要洞见。基于这一分析，我们提出了一种简单的几何正则化策略，该策略可提升监督预训练的可迁移性。因此，我们的工作既揭示了三维迁移学习的具体挑战，也提供了克服这些挑战的策略。