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。

用于形变形态分析的通用局部特征预训练

Generalizable Local Feature Pre-training for Deformable Shape Analysis

Many algorithms for the computation of correspondences between deformable
shapes rely on some variant of nearest neighbor matching in a descriptor space.
Such are, for example, various point-wise correspondence recovery algorithms
used as a post-processing stage in the functional correspondence framework.
Such frequently used techniques implicitly make restrictive assumptions (e.g.,
near-isometry) on the considered shapes and in practice suffer from lack of
accuracy and result in poor surjectivity. We propose an alternative recovery
technique capable of guaranteeing a bijective correspondence and producing
significantly higher accuracy and smoothness. Unlike other methods our approach
does not depend on the assumption that the analyzed shapes are isometric. We
derive the proposed method from the statistical framework of kernel density
estimation and demonstrate its performance on several challenging deformable 3D
shape matching datasets.

通过核密度估计的统计框架，我们提出了一种不依赖于形状同构的替代恢复技术，能够保证双向对应并产生更高的精度和平滑度，并在多个具有挑战性的可变形 3D 形状匹配数据集上展示其性能。