SEE Challenge 2026: Event-Guided Brightness Adjustment Across a Broad Illumination Range

Yunfan Lu; Mingchao Xu; Hanyu Zhou; Shaoyu Liu; Haoyue Liu; Peiqi Duan; Shihan Peng; Yinqiang Zheng; Boxin Shi; Gim Hee Lee; Hui Xiong; Davide Scaramuzza

arXiv · 2609.29347

SEE Challenge 2026: Event-Guided Brightness Adjustment Across a Broad Illumination Range

Yunfan Lu·Mingchao Xu·Hanyu Zhou·Shaoyu Liu·Haoyue Liu·Peiqi Duan·Shihan Peng·Yinqiang Zheng·Boxin Shi·Gim Hee Lee·Hui Xiong·Davide Scaramuzza

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Abstract

Event cameras provide a high dynamic range and preserve brightness-change cues in lighting conditions where conventional RGB frames may be noisy or saturated. To benchmark event-guided restoration across a broad illumination range, we organized the SEE Challenge 2026 with the Event-Based Multimodal Vision Workshop at ECCV 2026. The task conditions restoration on one or more RGB frames, synchronized events, and a scalar target-brightness statistic provided by the organizers. It uses SEE-600K, which contains 610,126 image-event observations from 202 real-world scenes spanning low-light, normal-light, and high-light conditions with illumination variations of up to 1,000$\times$. The challenge follows an open-system protocol: participants may use different temporal contexts, architectures, pretrained weights, test-time augmentation, and post-processing strategies. PSNR determines the ranking, and SSIM is reported as a secondary metric. Around 70 teams registered interest and 15 valid CodaBench submissions were received. Six distinct teams completed organizer-side identity and technical verification, provided method descriptions, checkpoints, inference code, and instructions, and are included in the verified open-system ranking reported here. Beyond the ranking, this report analyzes exposure subsets, semantically distinct test cases, a shared failure pattern, system design choices, and inference strategies. The top systems obtain closely spaced average scores, while the best-performing method varies across cases and metrics; under severe underexposure, all verified systems retain visible local errors.

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Yunfan Lu, Mingchao Xu, Hanyu Zhou, Shaoyu Liu, Haoyue Liu, Peiqi Duan, Shihan Peng, Yinqiang Zheng, Boxin Shi, Gim Hee Lee, Hui Xiong, Davide Scaramuzza. 2026-09-24. SEE Challenge 2026: Event-Guided Brightness Adjustment Across a Broad Illumination Range. https://arxiv.org/abs/2609.29347

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