# Learning Single-Image Depth from Videos using Quality Assessment Networks

arXiv: https://arxiv.org/abs/1806.09573 · HTML: https://mlsanity.com/paper/1806.09573/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2018.

## Built on by (most cited first, 25 of 25)

- [Unsupervised Scale-consistent Depth Learning from Video](https://mlsanity.com/paper/2105.11610/) (2021)
- [AffordanceLLM: Grounding Affordance from Vision Language Models](https://mlsanity.com/paper/2401.06341/) (2024)
- [Hierarchical Normalization for Robust Monocular Depth Estimation](https://mlsanity.com/paper/2210.09670/) (2022)
- [SC-DepthV3: Robust Self-supervised Monocular Depth Estimation for Dynamic Scenes](https://mlsanity.com/paper/2211.03660/) (2022)
- [RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions](https://mlsanity.com/paper/2310.15171/) (2023)
- [Virtual Normal: Enforcing Geometric Constraints for Accurate and Robust Depth Prediction](https://mlsanity.com/paper/2103.04216/) (2021)
- [Towards Accurate Reconstruction of 3D Scene Shape from A Single Monocular Image](https://mlsanity.com/paper/2208.13241/) (2022)
- [Single Image Depth Estimation: An Overview](https://mlsanity.com/paper/2104.06456/) (2021)
- [Understanding 3D Object Interaction from a Single Image](https://mlsanity.com/paper/2305.09664/) (2023)
- [Structure from Silence: Learning Scene Structure from Ambient Sound](https://mlsanity.com/paper/2111.05846/) (2021)
- [Improving Monocular Visual Odometry Using Learned Depth](https://mlsanity.com/paper/2204.01268/) (2022)
- [Real-time Monocular Depth Estimation with Sparse Supervision on Mobile](https://mlsanity.com/paper/2105.12053/) (2021)
- [A Survey on RGB-D Datasets](https://mlsanity.com/paper/2201.05761/) (2022)
- [Towards 3D Scene Reconstruction from Locally Scale-Aligned Monocular Video Depth](https://mlsanity.com/paper/2202.01470/) (2022)
- [Towards Domain-agnostic Depth Completion](https://mlsanity.com/paper/2207.14466/) (2022)
- [DnD: Dense Depth Estimation in Crowded Dynamic Indoor Scenes](https://mlsanity.com/paper/2108.05615/) (2021)
- [Recognizing Scenes from Novel Viewpoints](https://mlsanity.com/paper/2112.01520/) (2021)
- [Scale-Invariant Monocular Depth Estimation via SSI Depth](https://mlsanity.com/paper/2406.09374/) (2024)
- [D4D: An RGBD diffusion model to boost monocular depth estimation](https://mlsanity.com/paper/2403.07516/) (2024)
- [Floating No More: Object-Ground Reconstruction from a Single Image](https://mlsanity.com/paper/2407.18914/) (2024)
- [Predicting 3D shapes, masks, and properties of materials, liquids, and objects inside transparent containers, using the TransProteus CGI dataset](https://mlsanity.com/paper/2109.07577/) (2021)
- [DynOcc: Learning Single-View Depth from Dynamic Occlusion Cues](https://mlsanity.com/paper/2103.16706/) (2021)
- [360 in the Wild: Dataset for Depth Prediction and View Synthesis](https://mlsanity.com/paper/2406.18898/) (2024)
- [Pseudo Supervised Monocular Depth Estimation with Teacher-Student Network](https://mlsanity.com/paper/2110.11545/) (2021)
- [RLSEP: Learning Label Ranks for Multi-label Classification](https://mlsanity.com/paper/2212.04022/) (2022)

## Abstract (arXiv)

Depth estimation from a single image in the wild remains a challenging problem. One main obstacle is the lack of high-quality training data for images in the wild. In this paper we propose a method to automatically generate such data through Structure-from-Motion (SfM) on Internet videos. The core of this method is a Quality Assessment Network that identifies high-quality reconstructions obtained from SfM. Using this method, we collect single-view depth training data from a large number of YouTube videos and construct a new dataset called YouTube3D. Experiments show that YouTube3D is useful in training depth estimation networks and advances the state of the art of single-view depth estimation in the wild.