Workshop Focus Areas

• Image de-hazing, de-raining, and de-snowing
• Shadow, glare, and reflection removal
• Low-light and nighttime image enhancement
• Domain adaptation for weather and illumination shifts
• Sensor fusion under degraded visibility
• Uncertainty-aware semantic segmentation

Adverse weather and lighting conditions present some of the most persistent challenges for computer vision systems deployed outdoors. Rain, snow, fog, and ice can dramatically reduce visibility, while low-light environments, nighttime scenes, and harsh glare introduce artifacts that degrade sensor data. Automated systems such as autonomous vehicles and surveillance networks must interpret their surroundings reliably across all these scenarios, yet many state-of-the-art algorithms are developed and benchmarked primarily under favorable, clear-weather conditions. Bridging this gap between controlled evaluation and real-world variability is essential for building systems that perform safely and consistently throughout the year.

Image restoration techniques lie at the heart of research into adverse weather robustness. Methods for de-hazing, de-raining, and de-snowing aim to recover clean scene representations from degraded inputs, while shadow, glare, and reflection removal tackle localized visibility obstructions that can confuse downstream perception modules. Low-light and nighttime image enhancement further extends the operational envelope of vision systems, enabling them to function when natural illumination is scarce. These restoration approaches are increasingly being integrated into end-to-end learning pipelines, where enhancement and high-level understanding are optimized jointly rather than treated as separate, sequential stages.

Domain adaptation has emerged as a critical strategy for handling the distribution shifts caused by weather and illumination changes. Models trained extensively on clear, daytime imagery often fail when confronted with foggy mornings, snowy roads, or twilight conditions, because the statistical properties of the input data differ substantially from the training domain. Techniques that align feature representations across weather conditions, or that generate synthetic adverse-condition training samples, help close this performance gap without requiring costly pixel-level annotations for every possible environmental variation a system might encounter.

Sensor fusion offers a promising pathway toward greater resilience under degraded visibility. While cameras struggle in heavy fog or direct glare, complementary modalities such as lidar, radar, and thermal imaging can provide useful signals that persist when one sensor type is compromised. Designing fusion architectures that intelligently weigh and combine information from multiple sources, particularly when some sensors are partially or temporarily blinded, is an active area of investigation. The goal is to create perception stacks that degrade gracefully rather than failing catastrophically when individual sensors encounter challenging conditions.

Uncertainty-aware perception is gaining traction as a necessary component of vision systems operating in adverse environments. Rather than producing a single deterministic output and assuming it is correct, uncertainty-aware models can express confidence levels in their predictions, flag ambiguous regions, and signal when conditions exceed safe operating limits. This capability is especially valuable for safety-critical applications, where knowing what the system does not know can be as important as what it does know. Probabilistic segmentation and detection frameworks are being developed to quantify both aleatoric uncertainty from sensor noise and epistemic uncertainty from unfamiliar scenarios.

Benchmarking and evaluation practices are evolving to better reflect the diversity of real-world operating conditions. Traditional datasets often underrepresent adverse weather and challenging lighting, leading to inflated performance metrics that do not translate to practical deployments. New benchmarks are being curated with deliberate inclusion of rain, snow, fog, nighttime, and glare scenarios, accompanied by evaluation protocols that measure robustness alongside raw accuracy. These efforts help the research community identify failure modes, track progress on weather-resilient vision, and incentivize the development of algorithms that work across all seasons.

The broader vision community is increasingly recognizing that robustness to environmental variation is not a niche concern but a fundamental requirement for any system intended to operate outdoors. Workshops, challenges, and collaborative initiatives are bringing together researchers from image restoration, domain adaptation, sensor fusion, and safety engineering to tackle these interconnected problems. By fostering dialogue across these subfields and encouraging the release of diverse, challenging datasets, the community is laying the groundwork for a generation of vision systems that remain reliable whether the sun is shining, the snow is falling, or the fog is rolling in.