Self-Supervised Monocular Depth Estimation in Dark Environments: Compensating for Data Distribution Differences

Core Concepts

A self-supervised monocular depth estimation framework that does not use any nighttime images during training, but instead compensates for key day-night differences in photometric and noise distributions to enable effective training on daytime images.

Abstract

The paper proposes a self-supervised monocular depth estimation framework that can effectively train on daytime images and generalize to challenging nighttime scenes, without using any nighttime images during training. Key insights: Nighttime self-supervised depth estimation is challenging due to violations of the photometric consistency assumption under complex lighting conditions. The authors identify two key day-night differences: photometric distribution and noise distribution. They propose Brightness Peak Generator (BPG) to model the non-uniform photometric distribution at night, and Imaging Noise Generator (ING) to model the increased noise in nighttime scenes. By compensating the day image distribution with these physical priors, the framework can be trained in a one-stage self-supervised manner using only daytime images. Qualitative and quantitative results demonstrate that the proposed method achieves state-of-the-art performance on the challenging nuScenes-Night and RobotCar-Night datasets, outperforming existing domain adaptation and photometric loss repair methods.

Stats

The system gain K has a positive relationship with the noise intensity in the imaging noise model. The scale factor s is estimated from the predicted unscaled depth map and the actual camera height. The number of light sources NF and the intensity scale rate sF follow log-uniform distributions.

Quotes

"Though no nighttime images are considered during training, qualitative and quantitative results demonstrate that our method achieves SoTA depth estimating results on the challenging nuScenes-Night and RobotCar-Night compared with existing methods." "Focusing on the difference in lighting conditions, the dissimilarities in photometric and noise distribution are located as two key components."

Key Insights Distilled From

Self-Supervised Monocular Depth Estimation in the Dark: Towards Data Distribution Compensation

by Haolin Yang,... at arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.13854.pdf

Deeper Inquiries

How could the proposed data distribution compensation framework be extended to other computer vision tasks beyond monocular depth estimation that also suffer from domain shift issues

The proposed data distribution compensation framework can be extended to other computer vision tasks beyond monocular depth estimation by adapting the concept of compensating for data distribution shifts using physical priors. Here are some ways this framework could be applied to other tasks: Semantic Segmentation: In semantic segmentation, different lighting conditions or camera settings can lead to domain shifts that affect model performance. By incorporating physical priors to simulate these shifts and compensate for them during training, the model can learn to generalize better across different domains. Object Detection: Similar to semantic segmentation, object detection models can benefit from data distribution compensation to handle variations in lighting, background clutter, or object appearance. By using physical priors to model these variations and adjust the training data distribution, the model can improve its robustness to domain shifts. Image Translation: Tasks like image-to-image translation often suffer from domain shift issues when translating images between different domains (e.g., day to night, sketch to photo). By leveraging physical priors to simulate domain-specific characteristics and adjusting the data distribution accordingly, the translation model can learn to generate more realistic and accurate results. Instance Segmentation: Instance segmentation tasks require precise delineation of individual objects in an image, which can be challenging in the presence of domain shifts. By incorporating data distribution compensation techniques based on physical priors, the model can learn to segment objects accurately across different domains by accounting for variations in lighting, textures, and noise. Overall, the key idea is to identify the specific domain differences that impact model performance in a given computer vision task and use physical priors to simulate and compensate for these differences during training.

What are the potential limitations of the physical priors-based approach compared to learning-based domain adaptation methods, and how could they be addressed

While the physical priors-based approach offers controllability, directionality, and explainability in compensating for data distribution shifts, it also has some potential limitations compared to learning-based domain adaptation methods: Limited Generalization: Physical priors are based on assumptions and models that may not capture all the complexities of real-world data distribution shifts. This could limit the generalization ability of the model to unseen domains that deviate significantly from the simulated priors. Model Complexity: Designing accurate physical priors for all possible variations in data distribution can be challenging and may require domain-specific knowledge. This could lead to increased model complexity and computational costs. Adaptability: Physical priors may not easily adapt to dynamic changes in the data distribution or new unseen scenarios. Learning-based domain adaptation methods, on the other hand, can adapt to new domains by updating model parameters based on the available data. To address these limitations, a hybrid approach that combines physical priors with learning-based adaptation techniques could be beneficial. By leveraging the strengths of both approaches, the model can benefit from the controllability of physical priors while also learning to adapt to new domains through data-driven adjustments.

Could the insights from this work inspire new self-supervised learning techniques that can effectively leverage auxiliary information or physical models to overcome data distribution challenges in other domains

The insights from this work can inspire new self-supervised learning techniques that effectively leverage auxiliary information or physical models to overcome data distribution challenges in other domains by: Incorporating Auxiliary Information: By integrating auxiliary information such as sensor data, environmental conditions, or contextual cues into the self-supervised learning framework, models can learn to adapt to different data distributions and improve generalization performance. Utilizing Physical Models: Leveraging physical models or priors specific to the task domain can help simulate variations in data distribution and guide the training process. This can enhance the model's ability to handle domain shifts and improve robustness. Exploring Multi-Task Learning: By incorporating multiple related tasks into the self-supervised learning framework, models can learn to extract useful features and representations that are invariant to data distribution shifts. This multi-task approach can enhance the model's ability to generalize across different domains. Dynamic Adaptation: Developing techniques that allow the model to dynamically adapt to changes in the data distribution during training or inference can further improve performance in challenging scenarios where domain shifts occur frequently. Overall, by drawing inspiration from the data distribution compensation framework proposed in this work and exploring innovative ways to incorporate auxiliary information and physical models, new self-supervised learning techniques can be developed to address data distribution challenges in various computer vision tasks.