# Moving Indoor: Unsupervised Video Depth Learning in Challenging Environments

arXiv: https://arxiv.org/abs/1910.08898 · HTML: https://mlsanity.com/paper/1910.08898/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2019.

## Built on by (most cited first, 30 of 31)

- [SurroundOcc: Multi-Camera 3D Occupancy Prediction for Autonomous Driving](https://mlsanity.com/paper/2303.09551/) (2023)
- [SurroundDepth: Entangling Surrounding Views for Self-Supervised Multi-Camera Depth Estimation](https://mlsanity.com/paper/2204.03636/) (2022)
- [Unsupervised Scale-consistent Depth Learning from Video](https://mlsanity.com/paper/2105.11610/) (2021)
- [Dense Optical Tracking: Connecting the Dots](https://mlsanity.com/paper/2312.00786/) (2023)
- [SC-DepthV3: Robust Self-supervised Monocular Depth Estimation for Dynamic Scenes](https://mlsanity.com/paper/2211.03660/) (2022)
- [Toward Practical Monocular Indoor Depth Estimation](https://mlsanity.com/paper/2112.02306/) (2021)
- [OccNeRF: Advancing 3D Occupancy Prediction in LiDAR-Free Environments](https://mlsanity.com/paper/2312.09243/) (2023)
- [MonoIndoor: Towards Good Practice of Self-Supervised Monocular Depth Estimation for Indoor Environments](https://mlsanity.com/paper/2107.12429/) (2021)
- [StructDepth: Leveraging the structural regularities for self-supervised indoor depth estimation](https://mlsanity.com/paper/2108.08574/) (2021)
- [Video Autoencoder: self-supervised disentanglement of static 3D structure and motion](https://mlsanity.com/paper/2110.02951/) (2021)
- [WorDepth: Variational Language Prior for Monocular Depth Estimation](https://mlsanity.com/paper/2404.03635/) (2024)
- [StructNeRF: Neural Radiance Fields for Indoor Scenes with Structural Hints](https://mlsanity.com/paper/2209.05277/) (2022)
- [S2R-DepthNet: Learning a Generalizable Depth-specific Structural Representation](https://mlsanity.com/paper/2104.00877/) (2021)
- [RSA: Resolving Scale Ambiguities in Monocular Depth Estimators through Language Descriptions](https://mlsanity.com/paper/2410.02924/) (2024)
- [Generalizing to the Open World: Deep Visual Odometry with Online Adaptation](https://mlsanity.com/paper/2103.15279/) (2021)
- [Self-distilled Feature Aggregation for Self-supervised Monocular Depth Estimation](https://mlsanity.com/paper/2209.07088/) (2022)
- [GasMono: Geometry-Aided Self-Supervised Monocular Depth Estimation for Indoor Scenes](https://mlsanity.com/paper/2309.16019/) (2023)
- [PLNet: Plane and Line Priors for Unsupervised Indoor Depth Estimation](https://mlsanity.com/paper/2110.05839/) (2021)
- [MonoIndoor++:Towards Better Practice of Self-Supervised Monocular Depth Estimation for Indoor Environments](https://mlsanity.com/paper/2207.08951/) (2022)
- [MonoNeRF: Learning Generalizable NeRFs from Monocular Videos without Camera Pose](https://mlsanity.com/paper/2210.07181/) (2022)
- [Fusing the Old with the New: Learning Relative Camera Pose with Geometry-Guided Uncertainty](https://mlsanity.com/paper/2104.08278/) (2021)
- [Deeper into Self-Supervised Monocular Indoor Depth Estimation](https://mlsanity.com/paper/2312.01283/) (2023)
- [Adjacent-view Transformers for Supervised Surround-view Depth Estimation](https://mlsanity.com/paper/2303.07759/) (2023)
- [MonoSelfRecon: Purely Self-Supervised Explicit Generalizable 3D Reconstruction of Indoor Scenes from Monocular RGB Views](https://mlsanity.com/paper/2404.06753/) (2024)
- [Meta-Optimization for Higher Model Generalizability in Single-Image Depth Prediction](https://mlsanity.com/paper/2305.07269/) (2023)
- [BEVScope: Enhancing Self-Supervised Depth Estimation Leveraging Bird's-Eye-View in Dynamic Scenarios](https://mlsanity.com/paper/2306.11598/) (2023)
- [GAM-Depth: Self-Supervised Indoor Depth Estimation Leveraging a Gradient-Aware Mask and Semantic Constraints](https://mlsanity.com/paper/2402.14354/) (2024)
- [Towards Continual, Online, Self-Supervised Depth](https://mlsanity.com/paper/2103.00369/) (2021)
- [InSpaceType: Reconsider Space Type in Indoor Monocular Depth Estimation](https://mlsanity.com/paper/2309.13516/) (2023)
- [SS3D: End2End Self-Supervised 3D from Web Videos](https://mlsanity.com/paper/2604.22686/) (2026)

## Abstract (arXiv)

Recently unsupervised learning of depth from videos has made remarkable progress and the results are comparable to fully supervised methods in outdoor scenes like KITTI. However, there still exist great challenges when directly applying this technology in indoor environments, e.g., large areas of non-texture regions like white wall, more complex ego-motion of handheld camera, transparent glasses and shiny objects. To overcome these problems, we propose a new optical-flow based training paradigm which reduces the difficulty of unsupervised learning by providing a clearer training target and handles the non-texture regions. Our experimental evaluation demonstrates that the result of our method is comparable to fully supervised methods on the NYU Depth V2 benchmark. To the best of our knowledge, this is the first quantitative result of purely unsupervised learning method reported on indoor datasets.