STEPS: Joint Self-supervised Nighttime Image Enhancement and Depth Estimation (2302.01334v1)

Abstract: Self-supervised depth estimation draws a lot of attention recently as it can promote the 3D sensing capabilities of self-driving vehicles. However, it intrinsically relies upon the photometric consistency assumption, which hardly holds during nighttime. Although various supervised nighttime image enhancement methods have been proposed, their generalization performance in challenging driving scenarios is not satisfactory. To this end, we propose the first method that jointly learns a nighttime image enhancer and a depth estimator, without using ground truth for either task. Our method tightly entangles two self-supervised tasks using a newly proposed uncertain pixel masking strategy. This strategy originates from the observation that nighttime images not only suffer from underexposed regions but also from overexposed regions. By fitting a bridge-shaped curve to the illumination map distribution, both regions are suppressed and two tasks are bridged naturally. We benchmark the method on two established datasets: nuScenes and RobotCar and demonstrate state-of-the-art performance on both of them. Detailed ablations also reveal the mechanism of our proposal. Last but not least, to mitigate the problem of sparse ground truth of existing datasets, we provide a new photo-realistically enhanced nighttime dataset based upon CARLA. It brings meaningful new challenges to the community. Codes, data, and models are available at https://github.com/ucaszyp/STEPS.

• The paper's main contribution is a joint self-supervised framework that integrates nighttime image enhancement with depth estimation.
• It employs an uncertain pixel masking strategy to mitigate photometric inconsistencies, significantly improving depth accuracy in low-light conditions.
• The framework achieves state-of-the-art performance on nuScenes and RobotCar datasets while offering a cost-effective solution for autonomous vehicles.

Joint Self-supervised Nighttime Image Enhancement and Depth Estimation

The paper "STEPS: Joint Self-supervised Nighttime Image Enhancement and Depth Estimation" proposes a novel methodology for addressing the challenges associated with self-supervised depth estimation during nighttime driving scenarios. The inherent difficulties faced in such conditions stem from the photometric inconsistencies prevalent at night, which impede effective depth estimation—a critical component for the reliable operation of autonomous vehicles. The proposed method innovatively combines nighttime image enhancement with depth estimation to form a synergistic framework that does not rely on ground truth data for either task.

Methodological Approach

The authors introduce the STEPS framework, which is structured around two key components: an image enhancement module and a depth estimation module. Both modules are interconnected through a shared self-supervised learning process. Instead of relying on supervised learning protocols, which can be limited by dataset constraints and biases, STEPS implements a self-supervised learning strategy that capitalizes on the intrinsic relationship between image enhancement and depth estimation.

Central to their approach is the use of a novel uncertain pixel masking strategy, which manages the dual problem of underexposed and overexposed regions in nighttime images. This is achieved by fitting a bridge-shaped curve to the illumination map distribution, allowing for the suppression of these regions and facilitating a natural integration of the two self-supervised tasks. The result is a framework capable of more accurately predicting depth by focusing on usable photometric information while discounting unreliable data from problematic exposure areas.

Empirical Validation

The authors validate their approach using two established datasets: nuScenes and RobotCar. The STEPS framework demonstrates state-of-the-art performance in these benchmarks, particularly excelling in environments that feature complex lighting conditions typical of nighttime driving scenarios. Detailed ablation studies provide insight into the proposed model’s inner workings and highlight the effectiveness of the uncertain pixel masking strategy in improving depth accuracy.

Additionally, to further address the issue of sparse ground truth data, the paper introduces a new photo-realistically enhanced nighttime dataset based on CARLA, a simulation environment. This new dataset offers dense depth ground truths, presenting novel challenges that enrich the research community's resources for nighttime driving simulation and assessment.

Practical and Theoretical Implications

Practically, this research offers a significant step toward improving the safety and reliability of autonomous vehicles operating under low-light conditions, a prevalent challenge in real-world driving scenarios. By reducing reliance on expensive LiDAR systems, the framework presents a cost-effective alternative that harnesses the improved potential of image-based sensing systems.

From a theoretical standpoint, the integration of image enhancement with depth estimation in a joint framework, without requiring explicit ground truth, provides a promising avenue for future self-supervised learning approaches. The idea of utilizing intermediate outputs such as illumination maps to address practical challenges in depth estimation may inspire further research into similar synergies in other domains.

Future Directions

While the STEPS framework significantly advances the field of nighttime depth estimation, there is room for further exploration. Future research could investigate the integration of real-time processing capabilities, enhancing the system's applicability to real-world autonomous systems. Additionally, further studies could focus on extending the concept of data-driven mask generation to various environmental conditions beyond nighttime scenarios, potentially widening the applicability of self-supervised learning techniques for autonomous sensing tasks.

In summary, the paper provides an insightful contribution to the landscape of nighttime depth estimation, effectively merging image enhancement with depth estimation and paving the way for robust autonomous vehicle operation in low-light settings through innovative self-supervised learning methodologies.