Establishing a correspondence between two non-rigidly deforming shapes is one
of the most fundamental problems in visual computing. Existing methods often
show weak resilience when presented with challenges innate to real-world data
such as noise, outliers, self-occlusion etc. On the other hand, auto-decoders
have demonstrated strong expressive power in learning geometrically meaningful
latent embeddings. However, their use in \emph{shape analysis} has been
limited. In this paper, we introduce an approach based on an auto-decoder
framework, that learns a continuous shape-wise deformation field over a fixed
template. By supervising the deformation field for points on-surface and
regularising for points off-surface through a novel \emph{Signed Distance
Regularisation} (SDR), we learn an alignment between the template and shape
\emph{volumes}. Trained on clean water-tight meshes, \emph{without} any
data-augmentation, we demonstrate compelling performance on compromised data
and real-world scans.

本文提出了一种基于自编码器框架的方法，学习固定模板上的连续形状变形场，通过表面内和表面外的点监督变形场，并通过一种新的有符号距离正则化方法，实现了非刚性形状对齐，在清洁水密网格上训练，在面临噪声，异常值和自遮挡等挑战时表现出色，为现实世界数据的形状分析提供基础。