Multi-Scale Local Implicit Keypoint Descriptor for Keypoint Matching

JongMin Lee, Eunhyeok Park, Sungjoo Yoo; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2023, pp. 6145-6154

Abstract


We investigate the potential of multi-scale descriptors which has been under-explored in the existing literature. At the pixel level, we propose utilizing both coarse and fine-grained descriptors and present a scale-aware method of negative sampling, which trains descriptors at different scales in a complementary manner, thereby improving their discriminative power. For sub-pixel level descriptors, we also propose adopting coordinate-based implicit modeling and learning the non-linearity of local descriptors on continuous-domain coordinates. Our experiments show that the proposed method achieves state-of-the-art performance on various tasks, i.e., image matching, relative pose estimation, and visual localization.

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[bibtex]
@InProceedings{Lee_2023_CVPR, author = {Lee, JongMin and Park, Eunhyeok and Yoo, Sungjoo}, title = {Multi-Scale Local Implicit Keypoint Descriptor for Keypoint Matching}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2023}, pages = {6145-6154} }