Despite the advances in extracting local features achieved by handcrafted and
learning-based descriptors, they are still limited by the lack of invariance to
non-rigid transformations. In this paper, we present a new approach to compute
features from still images that are robust to non-rigid deformations to
circumvent the problem of matching deformable surfaces and objects. Our
deformation-aware local descriptor, named DEAL, leverages a polar sampling and
a spatial transformer warping to provide invariance to rotation, scale, and
image deformations. We train the model architecture end-to-end by applying
isometric non-rigid deformations to objects in a simulated environment as
guidance to provide highly discriminative local features. The experiments show
that our method outperforms state-of-the-art handcrafted, learning-based image,
and RGB-D descriptors in different datasets with both real and realistic
synthetic deformable objects in still images. The source code and trained model
of the descriptor are publicly available at
this https URL

本文提出了一种新的局部特征计算方法 DEAL，通过极坐标采样和空间变换器抵消旋转、尺度和形变的影响，能够提供不变性。我们将模型应用于模拟环境中的几何体进行同构非刚性变形，以提供高度判别局部特征，并证明了该方法在不同数据集中相对于现有的局部特征和 RGB-D 描述符跑出了更好的结果。