  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
\url{https://github.com/pvnieo/vader}.
