Self-supervised Monocular Depth Estimation: Let's Talk About The Weather (2307.08357v1)

Abstract: Current, self-supervised depth estimation architectures rely on clear and sunny weather scenes to train deep neural networks. However, in many locations, this assumption is too strong. For example in the UK (2021), 149 days consisted of rain. For these architectures to be effective in real-world applications, we must create models that can generalise to all weather conditions, times of the day and image qualities. Using a combination of computer graphics and generative models, one can augment existing sunny-weather data in a variety of ways that simulate adverse weather effects. While it is tempting to use such data augmentations for self-supervised depth, in the past this was shown to degrade performance instead of improving it. In this paper, we put forward a method that uses augmentations to remedy this problem. By exploiting the correspondence between unaugmented and augmented data we introduce a pseudo-supervised loss for both depth and pose estimation. This brings back some of the benefits of supervised learning while still not requiring any labels. We also make a series of practical recommendations which collectively offer a reliable, efficient framework for weather-related augmentation of self-supervised depth from monocular video. We present extensive testing to show that our method, Robust-Depth, achieves SotA performance on the KITTI dataset while significantly surpassing SotA on challenging, adverse condition data such as DrivingStereo, Foggy CityScape and NuScenes-Night. The project website can be found here https://kieran514.github.io/Robust-Depth-Project/.

• The paper introduces a novel self-supervised approach employing bi-directional pseudo-supervision to enhance depth estimation under varied weather conditions.
• It leverages a hybrid architecture that integrates computer graphics, generative models, and innovative data augmentations to simulate adverse conditions during training.
• Results on KITTI and other datasets demonstrate significant improvements in depth accuracy, offering promising implications for autonomous driving and robotic navigation.

An Expert Review of Self-Supervised Monocular Depth Estimation in Diverse Weather Conditions

The paper "Self-supervised Monocular Depth Estimation: Let's Talk About The Weather" addresses the challenge inherent in self-supervised depth estimation from monocular video under varying environmental conditions. The authors highlight a significant gap in the existing methodologies: their reliance on clear, sunny conditions for training, which limits their real-world applicability given weather's unpredictable nature. The focus of the paper is on devising a system that can generalize across differing weather, times of day, and image qualities.

Methodology

The authors propose a novel approach involving the use of data augmentations to enhance robustness in depth estimation models. This is realized by a hybrid architecture that applies both computer graphics and generative models to simulate adverse weather conditions. Their solution introduces augmentations to the training dataset, which paradoxically has previously shown to hinder rather than help learning. To counter this, the authors introduce a pseudo-supervised loss, which leverages the consistency between unaugmented and augmented data to improve both depth and pose estimation without requiring labeled data.

Critical to their approach is the concept of bi-directional pseudo-supervision loss, which retains the intrinsic advantage of labeling-free learning while reaping some benefits typically associated with supervised learning paradigms. This loss exploits the agreement between depth estimations from original and augmented data, especially under different conditions, allowing for improved generalization and robustness. The authors also incorporate several recommendations for creating a robust augmentation framework and offer extensive experimental evidence of the model's efficacy.

Results and Evaluation

The results, tested on the venerable KITTI dataset, demonstrate that their Robust-Depth methodology not only meets current State-of-the-Art (SotA) performance under normal conditions but significantly outperforms existing methods under challenging conditions such as fog, night, and rain, as evidenced by datasets like DrivingStereo, Foggy CityScape, and NuScenes-Night. These experiments underscore the model's superior ability to maintain depth accuracy in adverse conditions compared to conventional methods which relied heavily on sunny datasets—showcasing an improvement in absolute relative error and RMS error among other metrics.

Implications

The implications of this work are considerable. Practically, the enhanced ability to estimate depth accurately under diverse weather conditions could significantly enhance autonomous driving systems, robotic navigation, and other applications requiring reliable depth perception. Theoretically, the bi-directional pseudo-supervised loss and a dependence on diverse augmentations propose a robust framework that can inspire future work in self-supervised learning domains.

Future Prospects

This work opens avenues for further research, particularly exploration within other areas of AI where robustness to varied input conditions is crucial. Future research could investigate more sophisticated augmentation techniques, real-time augmentation during training, or extend these methodologies to other vision tasks such as semantic segmentation.

Overall, this paper contributes substantially to the field of computer vision, providing a framework that could lead to more reliable systems capable of robust performance irrespective of environmental unpredictability.