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 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 regularizing for points off-surface through a novel Signed Distance Regularization (SDR), we learn an alignment between the template and shape volumes. Trained on clean watertight meshes, without any data-augmentation, we demonstrate compelling performance on compromised data and realworld scans.