首届视频去天气挑战赛，聚焦雨雪干扰下视频的清晰还原。
LoViF 2026 The First Challenge on Weather Removal in Videos
- 构建18段短视频数据集，含合成与真实帧对，支持去天气训练与评估。
- 37团队参与，5个有效提交，推动视频去天气技术发展。
- 强调视觉真实与时间一致性，适合视频修复与自动驾驶研究者。
本文回顾了2026年LoViF视频去天气挑战赛。该挑战旨在提升在雨、雪等恶劣天气下视频的恢复能力，要求方法在保持场景结构与运动动态的同时，实现视觉合理且时序一致的重建。为此，我们构建了一个新的短视频去天气数据集（WRV），包含18段视频，共1,216个合成帧与1,216个真实地面真值帧，分辨率为832×480，按1:1:1比例划分为训练、验证和测试集。挑战采用联合考虑保真度与感知质量的评估协议，吸引了37名参与者，共提交5份有效最终方案及配套说明。项目已在https://www.codabench.org/competitions/13462/公开。
原文摘要 · Abstract (English)
This paper presents a review of the LoViF 2026 Challenge on Weather Removal in Videos. The challenge encourages the development of methods for restoring clean videos from inputs degraded by adverse weather conditions such as rain and snow, with an emphasis on achieving visually plausible and temporally consistent results while preserving scene structure and motion dynamics. To support this task, we introduce a new short-form WRV dataset tailored for video weather removal. It consists of 18 videos 1,216 synthesized frames paired with 1,216 real-world ground-truth frames at a resolution of 832 x 480, and is split into training, validation, and test sets with a ratio of 1:1:1. The goal of this challenge is to advance robust and realistic video restoration under real-world weather conditions, with evaluation protocols that jointly consider fidelity and perceptual quality. The challenge attracted 37 participants and received 5 valid final submissions with corresponding fact sheets, contributing to progress in weather removal for videos. The project is publicly available at https://www.codabench.org/competitions/13462/.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。