# Learning Stereo from Single Images

arXiv: https://arxiv.org/abs/2008.01484 · HTML: https://mlsanity.com/paper/2008.01484/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2020.

## Built on by (most cited first, 30 of 45)

- [RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching](https://mlsanity.com/paper/2109.07547/) (2021)
- [CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching](https://mlsanity.com/paper/2104.04314/) (2021)
- [Single-View View Synthesis in the Wild with Learned Adaptive Multiplane Images](https://mlsanity.com/paper/2205.11733/) (2022)
- [Stereo Anywhere: Robust Zero-Shot Deep Stereo Matching Even Where Either Stereo or Mono Fail](https://mlsanity.com/paper/2412.04472/) (2024)
- [Revisiting Domain Generalized Stereo Matching Networks from a Feature Consistency Perspective](https://mlsanity.com/paper/2203.10887/) (2022)
- [NeRF-Supervised Deep Stereo](https://mlsanity.com/paper/2303.17603/) (2023)
- [GraftNet: Towards Domain Generalized Stereo Matching with a Broad-Spectrum and Task-Oriented Feature](https://mlsanity.com/paper/2204.00179/) (2022)
- [Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving](https://mlsanity.com/paper/2203.02112/) (2022)
- [Learning optical flow from still images](https://mlsanity.com/paper/2104.03965/) (2021)
- [StereoDiffusion: Training-Free Stereo Image Generation Using Latent Diffusion Models](https://mlsanity.com/paper/2403.04965/) (2024)
- [A Survey on Deep Stereo Matching in the Twenties](https://mlsanity.com/paper/2407.07816/) (2024)
- [Scene-Aware 3D Multi-Human Motion Capture from a Single Camera](https://mlsanity.com/paper/2301.05175/) (2023)
- [Revisiting Depth Completion from a Stereo Matching Perspective for Cross-domain Generalization](https://mlsanity.com/paper/2312.09254/) (2023)
- [Detection Based Part-level Articulated Object Reconstruction from Single RGBD Image](https://mlsanity.com/paper/2504.03177/) (2025)
- [Federated Online Adaptation for Deep Stereo](https://mlsanity.com/paper/2405.14873/) (2024)
- [3D Hierarchical Refinement and Augmentation for Unsupervised Learning of Depth and Pose from Monocular Video](https://mlsanity.com/paper/2112.03045/) (2021)
- [Semi-Supervised Learning with Mutual Distillation for Monocular Depth Estimation](https://mlsanity.com/paper/2203.09737/) (2022)
- [PanoDepth: A Two-Stage Approach for Monocular Omnidirectional Depth Estimation](https://mlsanity.com/paper/2202.01323/) (2022)
- [Rethinking Global Context in Crowd Counting](https://mlsanity.com/paper/2105.10926/) (2021)
- [Active Stereo Without Pattern Projector](https://mlsanity.com/paper/2309.12315/) (2023)
- [ImmersePro: End-to-End Stereo Video Synthesis Via Implicit Disparity Learning](https://mlsanity.com/paper/2410.00262/) (2024)
- [A Practical Stereo Depth System for Smart Glasses](https://mlsanity.com/paper/2211.10551/) (2022)
- [Semi-synthesis: A fast way to produce effective datasets for stereo matching](https://mlsanity.com/paper/2101.10811/) (2021)
- [DissolveStereo: Coarse Depth Injection for Zero-Shot Stereo Video Generation](https://mlsanity.com/paper/2411.14295/) (2024)
- [Towards Open-World Generation of Stereo Images and Unsupervised Matching](https://mlsanity.com/paper/2503.12720/) (2025)
- [Eye2Eye: A Simple Approach for Monocular-to-Stereo Video Synthesis](https://mlsanity.com/paper/2505.00135/) (2025)
- [Stereo Anything: Unifying Zero-shot Stereo Matching with Large-Scale Mixed Data](https://mlsanity.com/paper/2411.14053/) (2024)
- [Single-View View Synthesis with Self-Rectified Pseudo-Stereo](https://mlsanity.com/paper/2304.09527/) (2023)
- [Self-Assessed Generation: Trustworthy Label Generation for Optical Flow and Stereo Matching in Real-world](https://mlsanity.com/paper/2410.10453/) (2024)
- [Elastic3D: Controllable Stereo Video Conversion with Guided Latent Decoding](https://mlsanity.com/paper/2512.14236/) (2025)

## Abstract (arXiv)

Supervised deep networks are among the best methods for finding correspondences in stereo image pairs. Like all supervised approaches, these networks require ground truth data during training. However, collecting large quantities of accurate dense correspondence data is very challenging. We propose that it is unnecessary to have such a high reliance on ground truth depths or even corresponding stereo pairs. Inspired by recent progress in monocular depth estimation, we generate plausible disparity maps from single images. In turn, we use those flawed disparity maps in a carefully designed pipeline to generate stereo training pairs. Training in this manner makes it possible to convert any collection of single RGB images into stereo training data. This results in a significant reduction in human effort, with no need to collect real depths or to hand-design synthetic data. We can consequently train a stereo matching network from scratch on datasets like COCO, which were previously hard to exploit for stereo. Through extensive experiments we show that our approach outperforms stereo networks trained with standard synthetic datasets, when evaluated on KITTI, ETH3D, and Middlebury.