# ZeroStereo: Zero-shot Stereo Matching from Single Images

arXiv: https://arxiv.org/abs/2501.08654 · HTML: https://mlsanity.com/paper/2501.08654/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2025.

## Built on by (most cited first, 13 of 13)

- [Fast-FoundationStereo: Real-Time Zero-Shot Stereo Matching](https://mlsanity.com/paper/2512.11130/) (2025)
- [Jasmine: Harnessing Diffusion Prior for Self-supervised Depth Estimation](https://mlsanity.com/paper/2503.15905/) (2025)
- [BANet: Bilateral Aggregation Network for Mobile Stereo Matching](https://mlsanity.com/paper/2503.03259/) (2025)
- [PromptStereo: Zero-Shot Stereo Matching via Structure and Motion Prompts](https://mlsanity.com/paper/2603.01650/) (2026)
- [StereoSpace: Depth-Free Synthesis of Stereo Geometry via End-to-End Diffusion in a Canonical Space](https://mlsanity.com/paper/2512.10959/) (2025)
- [Faithfulness to Refusal: A Causal Audit of Neuron Selectors](https://mlsanity.com/paper/2607.05355/) (2026)
- [StereoVGGT: A Training-Free Visual Geometry Transformer for Stereo Vision](https://mlsanity.com/paper/2603.29368/) (2026)
- [DreamStereo: Towards Real-Time Stereo Inpainting for HD Videos](https://mlsanity.com/paper/2604.12270/) (2026)
- [MAGIC: Learning from Visibility Asymmetry for Unsupervised Stereo Matching](https://mlsanity.com/paper/2508.10838/) (2025)
- [MatchAttention: Embedding Explicit Matching Constraints into Attention for Efficient Stereo Matching](https://mlsanity.com/paper/2510.14260/) (2025)
- [Pip-Stereo: Progressive Iterations Pruner for Iterative Optimization based Stereo Matching](https://mlsanity.com/paper/2602.20496/) (2026)
- [GemDepth: Geometry-Embedded Features for 3D-Consistent Video Depth](https://mlsanity.com/paper/2605.10525/) (2026)
- [StereoGenBench: A Synthetic Multi-Camera Benchmark for Stereo Generation under Controlled Baseline Regimes](https://mlsanity.com/paper/2605.23237/) (2026)

## Abstract (arXiv)

State-of-the-art supervised stereo matching methods have achieved remarkable performance on various benchmarks. However, their generalization to real-world scenarios remains challenging due to the scarcity of annotated real-world stereo data. In this paper, we propose ZeroStereo, a novel stereo image generation pipeline for zero-shot stereo matching. Our approach synthesizes high-quality right images from arbitrary single images by leveraging pseudo disparities generated by a monocular depth estimation model. Unlike previous methods that address occluded regions by filling missing areas with neighboring pixels or random backgrounds, we fine-tune a diffusion inpainting model to recover missing details while preserving semantic structure. Additionally, we propose Training-Free Confidence Generation, which mitigates the impact of unreliable pseudo labels without additional training, and Adaptive Disparity Selection, which ensures a diverse and realistic disparity distribution while preventing excessive occlusion and foreground distortion. Experiments demonstrate that models trained with our pipeline achieve state-of-the-art zero-shot generalization across multiple datasets with only a dataset volume comparable to Scene Flow. Code: https://github.com/Windsrain/ZeroStereo.