Towards stronger illumination robustness of local feature detection and description based on auxiliary learning

Local feature detection and description play a crucial role in various computer vision tasks, including image matching. Variations in illumination conditions significantly affect the accuracy of these applications. However, existing methods inadequately address this issue. In this paper, a novel algorithm based on illumination auxiliary learning module (IALM) is introduced. Firstly, a new local feature extractor named illumination auxiliary Superpoint (IA-Superpoint) is established, based on the integration of IALM and Superpoint. Secondly, illumination-aware auxiliary training focuses on capturing the effects of illumination variations during feature extraction through tailored loss functions and jointly learning mechanisms. Lastly, in order to evaluate the illumination robustness of local features, a metric is proposed by simulating various illumination disturbances. Experiments on HPatches and RDNIM datasets demonstrate that the performance of local feature extraction is greatly improved by our method. Compared to the baseline method, the proposed method exhibits improvements in both mean matching accuracy and homography estimation.