HELIOS: From midnight to noon, continuous outdoor urban scene relighting
HELIOS enables urban scene relighting from midnight to noon using unpaired datasets, enhancing structural consistency.
Key Findings
Methodology
HELIOS utilizes unlabeled real-world datasets, integrating albedo-based conditioning into a cycle-consistent diffusion pipeline for urban scene relighting. GPS-derived solar angles enable fine control, ensuring structural stability.
Key Results
- HELIOS excels in night-to-day and day-to-night tasks, achieving FID scores of 74.65 and 100.72, significantly outperforming existing methods.
- In user studies, HELIOS received the highest preference rate and lowest failure rate, demonstrating its visual realism.
- Through albedo distillation strategy, HELIOS maintains structural consistency even in low-visibility nighttime conditions.
Significance
HELIOS addresses the generalization limitations of existing methods in real-world scenarios, especially in autonomous driving, providing more realistic scene editing and data augmentation capabilities, enhancing perception robustness.
Technical Contribution
HELIOS introduces unsupervised training strategies and albedo distillation to overcome limitations in nighttime scenes, offering new theoretical guarantees and engineering possibilities.
Novelty
HELIOS is the first to achieve day-night relighting under unsupervised conditions, combining albedo conditioning and solar angles for precise lighting control.
Limitations
- In extreme weather conditions, the model may struggle to maintain structural consistency.
- Requires high-performance computing resources for training.
Future Work
Future work could explore lighting transitions under various weather conditions and improve computational efficiency and training speed.
AI Executive Summary
HELIOS is a novel image relighting approach designed to tackle the challenges of lighting variations in autonomous driving scenarios. Existing methods rely on synthetic data or multi-illumination supervision, limiting their generalization to real-world scenarios. HELIOS trains on unlabeled real-world datasets without requiring paired images, integrating albedo-based conditioning into a cycle-consistent diffusion pipeline to ensure structural stability. GPS-derived solar angles enable fine control, allowing smooth and continuous lighting manipulation across the day-night cycle. Experimental results show HELIOS excels in night-to-day and day-to-night tasks, with user studies demonstrating its visual realism. Despite potential limitations in extreme weather conditions, HELIOS offers more realistic scene editing and data augmentation capabilities for autonomous driving. Future work will explore lighting transitions under various weather conditions and improve computational efficiency and training speed.
Deep Analysis
Background
Image relighting plays a crucial role in autonomous driving, where lighting variations pose significant challenges. Existing methods rely heavily on synthetic data or multi-illumination supervision, limiting their generalization to real-world scenarios. HELIOS trains on unlabeled real-world datasets without requiring paired images, integrating albedo-based conditioning into a cycle-consistent diffusion pipeline to ensure structural stability.
Core Problem
Lighting variations in autonomous driving scenarios are a significant challenge, with existing methods struggling to generalize to real-world conditions, especially at night where sensor noise and complex illumination effects differ significantly from synthetic data.
Innovation
HELIOS introduces unsupervised training strategies and albedo distillation to overcome limitations in nighttime scenes, combining GPS-derived solar angles for precise control, offering new theoretical guarantees and engineering possibilities.
Methodology
- �� Train on unlabeled real-world datasets
- �� Integrate albedo-based conditioning into cycle-consistent diffusion pipeline
- �� Use GPS-derived solar angles for fine control
- �� Introduce albedo distillation strategy to address nighttime structural consistency issues
Experiments
HELIOS is trained on nuScenes, Waymo, and Pandaset datasets, using 41k images, and evaluated through user studies, demonstrating its visual realism.
Results
HELIOS excels in night-to-day and day-to-night tasks, achieving FID scores of 74.65 and 100.72, significantly outperforming existing methods. In user studies, HELIOS received the highest preference rate and lowest failure rate.
Applications
HELIOS offers more realistic scene editing and data augmentation capabilities in autonomous driving, enhancing perception robustness and scene editing progress.
Limitations & Outlook
In extreme weather conditions, the model may struggle to maintain structural consistency. Requires high-performance computing resources for training.
Plain Language Accessible to non-experts
Imagine you're in a kitchen, where the lighting changes throughout the day. HELIOS acts like a smart lighting system, automatically adjusting the kitchen lights based on the time, ensuring you can see every corner clearly at any time. It doesn't need to know the exact layout of the kitchen beforehand, just adjusts based on the angle of the sun. Even at night, it maintains the kitchen's structural consistency, preventing any loss of visibility due to insufficient light. This technology helps autonomous vehicles drive safely under different lighting conditions, just like cooking in the kitchen is simple.
ELI14 Explained like you're 14
Hey, kids! Imagine you're playing a super cool game where the scenes change automatically based on the time of day. Morning is bright and sunny, while night is dimly lit. HELIOS is like the game's super smart lighting system, making the game scenes look super realistic anytime! Even at night, you can see every detail in the game clearly. This technology helps autonomous vehicles drive safely under different lighting conditions, just like exploring new worlds in the game is fun!
Glossary
Albedo
Albedo is the measure of how much light a surface reflects, usually represented as a dimensionless value.
Used in HELIOS for conditioning the image relighting process.
Cycle Consistency
Cycle consistency is a constraint ensuring an image can be reconstructed to its original state after domain translation.
Used to ensure structural stability.
Diffusion Model
A diffusion model is a generative model that creates images by gradually denoising.
Used for unsupervised image relighting.
Solar Angle
Solar angle is the elevation of the sun relative to the observer, used for lighting control.
Implemented via GPS data for precise lighting control.
Autonomous Driving
Autonomous driving refers to vehicles driving without human intervention.
HELIOS enhances visual realism in autonomous driving scenarios.
Open Questions Unanswered questions from this research
- 1 How to maintain structural consistency in extreme weather conditions? Current methods perform poorly in these scenarios.
- 2 How to improve computational efficiency and training speed? Currently requires high-performance computing resources.
Applications
Immediate Applications
Autonomous Driving Scene Editing
HELIOS can be used for real-time lighting adjustments in autonomous driving scenes, improving perception robustness.
Long-term Vision
All-weather Autonomous Driving
By improving HELIOS's performance, achieve all-weather autonomous driving, enhancing traffic safety.
Abstract
Modifying the illumination of driving images is a fundamental challenge, as most datasets are captured at specific times of day. Existing methods rely on synthetic data or paired multi-illumination supervision, which limits their generalization to the diverse and challenging conditions of real-world scenarios. To address this, we propose HELIOS, a novel image relighting approach that relies on unlabeled real-world datasets without requiring any paired images for training. Our approach integrates albedo-based conditioning into a cycle-consistent diffusion pipeline to prevent identity collapse and ensure accurate domain translation. To handle low-visibility nighttime conditions, we introduce a robust albedo distillation strategy that transfers structural stability from the daytime domain. Additionally, we replace traditional text prompts with a fine-grained control mechanism based on GPS-derived solar angles, enabling smooth and continuous lighting manipulation across the day-night cycle. Through extensive evaluation and a user study, we demonstrate that HELIOS produces structurally consistent and realistic results in both night-to-day and day-to-night tasks, outperforming state-of-the-art methods.