LoViF 2026 The First Challenge on Unified Removal of Raindrops and Reflections: Methods and Results
2026-08-24 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors review a competition designed to solve the problem of raindrops and reflections messing up camera views for self-driving cars on rainy days. The challenge attracted many participants, but only a dozen teams completed the final submission using a special real-world dataset called RDRF. The paper analyzes these submitted methods to identify what worked well and offers insights for future improvements. Overall, the report helps understand how to better remove raindrops and reflections from images.
autonomous drivingraindrop removalreflection removalimage degradationcomputer visiondatasetimage restorationchallenge competitionRDRF dataset
Authors
Zewei He, Xi Tong, Yu Chen, Xingyu Liu, Xin Li, Zepeng Wang, Jiagao Hu, Fuhao Li, Yuxuan Chen, Fei Wang, Daiguo Zhou, Minmin Yi, Chuanrui Zhang, Liwen Zhang, Yeongjin Jeong, Hyunjin Cho, Jiwon Lee, Minsang Kim, Jae Woong Soh, Jin-Hui Jiang, Rong-Lin Jian, Chih-Chung Hsu, Youngjin Oh, Junhyeong Kwon, Junyoung Park, Jae Hyun Park, Sung Ju Lee, Nam Ik Cho, Vishwajeet Shukla, Himanshu Baurai, Zhiqi Zhang, Kui Jiang, Zhaocheng Yu, Runzhe Li, Dawei Fan, Hao Li, Zhanshuo Zhang, Fan Ji, Jiangmeng Li, Xiongxin Tang, Fanjiang Xu, Shangquan Sun, Anh-Kiet Duong, Petra Gomez-Krämer, Jean-Michel Carozza, Ruibo Zhang, Dexiang Hong, Xinyan Liu, Shengeng Tang, Weidong Chen, Tzu-Hsuan Weng, Min-Te Sun
Abstract
This workshop paper comprehensively reviews the First Challenge on Unified Removal of Raindrops and Reflections. The challenge aims to address a frequently encountered practical problem in the field of autonomous driving, i.e., raindrop-reflection composite degradation on rainy days. This competition attracted 149 registered participants and received 12 valid final submissions with corresponding fact sheets, significantly contributing to the progress of unified removal of raindrops and reflections. All the methods are developed and evaluated on our real-shot RainDrop and ReFlection (RDRF) dataset. A detailed analysis of the submitted methods and corresponding results is provided in this report, which highlights effective approaches and provides interesting insights for future research.