Transfer learning is fundamental for addressing problems in settings with
little training data. While several transfer learning approaches have been
proposed in 3D, unfortunately, these solutions typically operate on an entire
3D object or even scene-level and thus, as we show, fail to generalize to new
classes, such as deformable organic shapes. In addition, there is currently a
lack of understanding of what makes pre-trained features transferable across
significantly different 3D shape categories. In this paper, we make a step
toward addressing these challenges. First, we analyze the link between feature
locality and transferability in tasks involving deformable 3D objects, while
also comparing different backbones and losses for local feature pre-training.
We observe that with proper training, learned features can be useful in such
tasks, but, crucially, only with an appropriate choice of the receptive field
size. We then propose a differentiable method for optimizing the receptive
field within 3D transfer learning. Jointly, this leads to the first learnable
features that can successfully generalize to unseen classes of 3D shapes such
as humans and animals. Our extensive experiments show that this approach leads
to state-of-the-art results on several downstream tasks such as segmentation,
shape correspondence, and classification. Our code is available at
https://github.com/pvnieo/vader.

本文介绍了一种学习可转移 3D 形状的特征的方法，通过优化感受野大小和比较不同的背景和损失函数来实现。实验证明，该方法可成功推广到人和动物等未见过的 3D 形状类别，并在分割、形状对应和分类等下游任务上实现了最先进的结果。