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Local feature extraction is a standard approach in computer vision fortackling important tasks such as image matching and retrieval. The coreassumption of most methods is that images undergo affine transformations,disregarding more complicated effects such as non-rigid deformations.Furthermore, incipient works tailored for non-rigid correspondence still relyon keypoint detectors designed for rigid transformations, hindering performancedue to the limitations of the detector. We propose DALF (Deformation-AwareLocal Features), a novel deformation-aware network for jointly detecting anddescribing keypoints, to handle the challenging problem of matching deformablesurfaces. All network components work cooperatively through a feature fusionapproach that enforces the descriptors' distinctiveness and invariance.Experiments using real deforming objects showcase the superiority of ourmethod, where it delivers 8% improvement in matching scores compared to theprevious best results. Our approach also enhances the performance of tworeal-world applications: deformable object retrieval and non-rigid 3D surfaceregistration. Code for training, inference, and applications are publiclyavailable at https://verlab.dcc.ufmg.br/descriptors/dalf_cvpr23.

Enhancing Deformable Local Features by Jointly Learning to Detect and Describe Keypoints