# Self-supervised Monocular Depth Estimation: Let's Talk About The Weather

arXiv: https://arxiv.org/abs/2307.08357 · HTML: https://mlsanity.com/paper/2307.08357/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 48 of its 63 references that are in the map.
- First posted on arXiv 17 Jul 2023.

## Builds on (most cited first, 30 of 48)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [Depth Map Prediction from a Single Image using a Multi-Scale Deep Network](https://mlsanity.com/paper/1406.2283/) (2014)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [Unsupervised Monocular Depth Estimation with Left-Right Consistency](https://mlsanity.com/paper/1609.03677/) (2016)
- [Unsupervised Learning of Depth and Ego-Motion from Video](https://mlsanity.com/paper/1704.07813/) (2017)
- [Deep Ordinal Regression Network for Monocular Depth Estimation](https://mlsanity.com/paper/1806.02446/) (2018)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [AdaBins: Depth Estimation using Adaptive Bins](https://mlsanity.com/paper/2011.14141/) (2020)
- [Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture](https://mlsanity.com/paper/1411.4734/) (2014)
- [3D Packing for Self-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/1905.02693/) (2019)
- [Semantic Foggy Scene Understanding with Synthetic Data](https://mlsanity.com/paper/1708.07819/) (2017)
- [From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation](https://mlsanity.com/paper/1907.10326/) (2019)
- [The Effectiveness of Data Augmentation in Image Classification using Deep Learning](https://mlsanity.com/paper/1712.04621/) (2017)
- [Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue](https://mlsanity.com/paper/1603.04992/) (2016)
- [Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming](https://mlsanity.com/paper/1907.07484/) (2019)
- [Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation](https://mlsanity.com/paper/1805.09806/) (2018)
- [The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth](https://mlsanity.com/paper/2104.14540/) (2021)
- [On the uncertainty of self-supervised monocular depth estimation](https://mlsanity.com/paper/2005.06209/) (2020)
- [BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimation](https://mlsanity.com/paper/2204.00987/) (2022)
- [MonoViT: Self-Supervised Monocular Depth Estimation with a Vision Transformer](https://mlsanity.com/paper/2208.03543/) (2022)
- [HR-Depth: High Resolution Self-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/2012.07356/) (2020)
- [Lite-Mono: A Lightweight CNN and Transformer Architecture for Self-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/2211.13202/) (2022)
- [Attention Attention Everywhere: Monocular Depth Prediction with Skip Attention](https://mlsanity.com/paper/2210.09071/) (2022)
- [How do neural networks see depth in single images?](https://mlsanity.com/paper/1905.07005/) (2019)
- [Unsupervised Scale-consistent Depth Learning from Video](https://mlsanity.com/paper/2105.11610/) (2021)
- [Self-Supervised Monocular Depth Estimation with Internal Feature Fusion](https://mlsanity.com/paper/2110.09482/) (2021)

## Built on by (most cited first, 30 of 34)

- [Synthetic-to-Real Self-supervised Robust Depth Estimation via Learning with Motion and Structure Priors](https://mlsanity.com/paper/2503.20211/) (2025)
- [WeatherDepth: Curriculum Contrastive Learning for Self-Supervised Depth Estimation under Adverse Weather Conditions](https://mlsanity.com/paper/2310.05556/) (2023)
- [Depth Anything at Any Condition](https://mlsanity.com/paper/2507.01634/) (2025)
- [Depth-Centric Dehazing and Depth-Estimation from Real-World Hazy Driving Video](https://mlsanity.com/paper/2412.11395/) (2024)
- [Stealing Stable Diffusion Prior for Robust Monocular Depth Estimation](https://mlsanity.com/paper/2403.05056/) (2024)
- [Dusk Till Dawn: Self-supervised Nighttime Stereo Depth Estimation using Visual Foundation Models](https://mlsanity.com/paper/2405.11158/) (2024)
- [Always Clear Depth: Robust Monocular Depth Estimation under Adverse Weather](https://mlsanity.com/paper/2505.12199/) (2025)
- [Hybrid-grained Feature Aggregation with Coarse-to-fine Language Guidance for Self-supervised Monocular Depth Estimation](https://mlsanity.com/paper/2510.09320/) (2025)
- [EC-Depth: Exploring the consistency of self-supervised monocular depth estimation in challenging scenes](https://mlsanity.com/paper/2310.08044/) (2023)
- [Improving Domain Generalization in Self-supervised Monocular Depth Estimation via Stabilized Adversarial Training](https://mlsanity.com/paper/2411.02149/) (2024)
- [Adverse Weather Optical Flow: Cumulative Homogeneous-Heterogeneous Adaptation](https://mlsanity.com/paper/2409.17001/) (2024)
- [ER-LoRA: Effective-Rank Guided Adaptation for Weather-Generalized Depth Estimation](https://mlsanity.com/paper/2509.00665/) (2025)
- [PLT-D3: A High-fidelity Dynamic Driving Simulation Dataset for Stereo Depth and Scene Flow](https://mlsanity.com/paper/2406.07667/) (2024)
- [BaseBoostDepth: Exploiting Larger Baselines For Self-supervised Monocular Depth Estimation](https://mlsanity.com/paper/2407.20437/) (2024)
- [Structure-Centric Robust Monocular Depth Estimation via Knowledge Distillation](https://mlsanity.com/paper/2410.06982/) (2024)
- [Learning Depth from Past Selves: Self-Evolution Contrast for Robust Depth Estimation](https://mlsanity.com/paper/2511.15167/) (2025)
- [AURORA-KITTI: Any-Weather Depth Completion and Denoising](https://mlsanity.com/paper/2603.14701/) (2026)
- [Unsupervised Monocular Depth Estimation Based on Hierarchical Feature-Guided Diffusion](https://mlsanity.com/paper/2406.09782/) (2024)
- [Multi-Modality Driven LoRA for Adverse Condition Depth Estimation](https://mlsanity.com/paper/2412.20162/) (2024)
- [Endoscopic Depth Estimation Based on Deep Learning: A Survey](https://mlsanity.com/paper/2507.20881/) (2025)
- [Adapting Depth Anything to Adverse Imaging Conditions with Events](https://mlsanity.com/paper/2601.02020/) (2026)
- [KPNDepth: Depth Estimation of Lane Images under Complex Rainy Environment](https://mlsanity.com/paper/2405.09964/) (2024)
- [ViewpointDepth: A New Dataset for Monocular Depth Estimation Under Viewpoint Shifts](https://mlsanity.com/paper/2409.17851/) (2024)
- [The ADUULM-360 Dataset -- A Multi-Modal Dataset for Depth Estimation in Adverse Weather](https://mlsanity.com/paper/2411.11455/) (2024)
- [PhysDepth: Plug-and-Play Physical Refinement for Monocular Depth Estimation in Challenging Environments](https://mlsanity.com/paper/2412.04666/) (2024)
- [Intrinsic Image Decomposition for Robust Self-supervised Monocular Depth Estimation on Reflective Surfaces](https://mlsanity.com/paper/2503.22209/) (2025)
- [DSOcc: Leveraging Depth Awareness and Semantic Aid to Boost Camera-Based 3D Semantic Occupancy Prediction](https://mlsanity.com/paper/2505.20951/) (2025)
- [LiDAR-Anchored Collaborative Distillation for Robust 2D Representations](https://mlsanity.com/paper/2602.12524/) (2026)
- [Boosting Robustness for All-Weather Self-Supervised Depth Estimation in Autonomous Driving](https://mlsanity.com/paper/2607.21526/) (2026)
- [JEPADepth: Masked Predictive Representation Learning for Self-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/2607.26600/) (2026)

## Abstract (arXiv)

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/.