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

2026-09-24

AI Summary

This study addresses the challenge of brightness restoration in RGB images captured under wide dynamic range illumination by proposing an event camera-assisted multimodal fusion framework. To support this, we introduce SEE-600K, a large-scale real-world dataset encompassing a thousand-fold variation in lighting conditions, alongside a rigorously validated open evaluation protocol and competition benchmark. Through this benchmark, fifteen valid submissions were collected, enabling a systematic assessment of six state-of-the-art algorithms using PSNR and SSIM metrics. Furthermore, we provide an in-depth analysis of performance discrepancies across varying exposure conditions and identify typical failure modes. Ultimately, this work establishes standardized data resources and a comprehensive evaluation framework to advance research in event-driven multimodal image restoration.

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.

Problem

• Event-guided restoration
• Brightness adjustment
• High dynamic range
• Broad illumination range

Innovation

• Event-guided restoration
• High dynamic range
• Brightness adjustment
• Multimodal vision
• Open-system benchmark