IF-Net: An Illumination-invariant Feature Network

Po Heng Chen, Zhao-Xu Luo, Zu-Kuan Huang, Chun Yang, Kuan-Wen Chen*


研究成果: Conference contribution同行評審

3 引文 斯高帕斯(Scopus)


Feature descriptor matching is a critical step is many computer vision applications such as image stitching, image retrieval and visual localization. However, it is often affected by many practical factors which will degrade its performance. Among these factors, illumination variations are the most influential one, and especially no previous descriptor learning works focus on dealing with this problem. In this paper, we propose IF-Net, aimed to generate a robust and generic descriptor under crucial illumination changes conditions. We find out not only the kind of training data important but also the order it is presented. To this end, we investigate several dataset scheduling methods and propose a separation training scheme to improve the matching accuracy. Further, we propose a ROI loss and hard-positive mining strategy along with the training scheme, which can strengthen the ability of generated descriptor dealing with large illumination change conditions. We evaluate our approach on public patch matching benchmark and achieve the best results compared with several state-of-the-arts methods. To show the practicality, we further evaluate IF-Net on the task of visual localization under large illumination changes scenes, and achieves the best localization accuracy.

主出版物標題2020 IEEE International Conference on Robotics and Automation, ICRA 2020
發行者Institute of Electrical and Electronics Engineers Inc.
出版狀態Published - 5月 2020
事件2020 IEEE International Conference on Robotics and Automation, ICRA 2020 - Paris, France
持續時間: 31 5月 202031 8月 2020


名字Proceedings - IEEE International Conference on Robotics and Automation


Conference2020 IEEE International Conference on Robotics and Automation, ICRA 2020


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