# Guided Stereo Matching

arXiv: https://arxiv.org/abs/1905.10107 · HTML: https://mlsanity.com/paper/1905.10107/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2019.

## Built on by (most cited first, 24 of 24)

- [Stereo Anywhere: Robust Zero-Shot Deep Stereo Matching Even Where Either Stereo or Mono Fail](https://mlsanity.com/paper/2412.04472/) (2024)
- [GraftNet: Towards Domain Generalized Stereo Matching with a Broad-Spectrum and Task-Oriented Feature](https://mlsanity.com/paper/2204.00179/) (2022)
- [OpenStereo: A Comprehensive Benchmark for Stereo Matching and Strong Baseline](https://mlsanity.com/paper/2312.00343/) (2023)
- [Revisiting Depth Completion from a Stereo Matching Perspective for Cross-domain Generalization](https://mlsanity.com/paper/2312.09254/) (2023)
- [S3: Learnable Sparse Signal Superdensity for Guided Depth Estimation](https://mlsanity.com/paper/2103.02396/) (2021)
- [Unsupervised confidence for LiDAR depth maps and applications](https://mlsanity.com/paper/2210.03118/) (2022)
- [Active Stereo Without Pattern Projector](https://mlsanity.com/paper/2309.12315/) (2023)
- [LiDAR-Event Stereo Fusion with Hallucinations](https://mlsanity.com/paper/2408.04633/) (2024)
- [Real-time Nonrigid Mosaicking of Laparoscopy Images](https://mlsanity.com/paper/2103.07414/) (2021)
- [LiDAR-guided Stereo Matching with a Spatial Consistency Constraint](https://mlsanity.com/paper/2202.09953/) (2022)
- [Multi-View Guided Multi-View Stereo](https://mlsanity.com/paper/2210.11467/) (2022)
- [Sensor-Guided Optical Flow](https://mlsanity.com/paper/2109.15321/) (2021)
- [Depth Estimation by Combining Binocular Stereo and Monocular Structured-Light](https://mlsanity.com/paper/2203.10493/) (2022)
- [Stereo-LiDAR Depth Estimation with Deformable Propagation and Learned Disparity-Depth Conversion](https://mlsanity.com/paper/2404.07545/) (2024)
- [R4D: Utilizing Reference Objects for Long-Range Distance Estimation](https://mlsanity.com/paper/2206.04831/) (2022)
- [Expansion of Visual Hints for Improved Generalization in Stereo Matching](https://mlsanity.com/paper/2211.00392/) (2022)
- [Visual Comfort Aware-Reinforcement Learning for Depth Adjustment of Stereoscopic 3D Images](https://mlsanity.com/paper/2104.06782/) (2021)
- [Depth on Demand: Streaming Dense Depth from a Low Frame Rate Active Sensor](https://mlsanity.com/paper/2409.08277/) (2024)
- [Sparse LiDAR Assisted Self-supervised Stereo Disparity Estimation](https://mlsanity.com/paper/2112.15355/) (2021)
- [MaDis-Stereo: Enhanced Stereo Matching via Distilled Masked Image Modeling](https://mlsanity.com/paper/2409.02846/) (2024)
- [Active Stereo in the Wild through Virtual Pattern Projection](https://mlsanity.com/paper/2406.04345/) (2024)
- [Rethinking the Key Factors for the Generalization of Remote Sensing Stereo Matching Networks](https://mlsanity.com/paper/2408.07613/) (2024)
- [Preserving Guidance in Cost-Volume Retrieval under Extreme LiDAR Sparsity in Iterative Stereo](https://mlsanity.com/paper/2507.19738/) (2025)
- [Domain Generalized Stereo Matching with Uncertainty-guided Data Augmentation](https://mlsanity.com/paper/2508.01303/) (2025)

## Abstract (arXiv)

Stereo is a prominent technique to infer dense depth maps from images, and deep learning further pushed forward the state-of-the-art, making end-to-end architectures unrivaled when enough data is available for training. However, deep networks suffer from significant drops in accuracy when dealing with new environments. Therefore, in this paper, we introduce Guided Stereo Matching, a novel paradigm leveraging a small amount of sparse, yet reliable depth measurements retrieved from an external source enabling to ameliorate this weakness. The additional sparse cues required by our method can be obtained with any strategy (e.g., a LiDAR) and used to enhance features linked to corresponding disparity hypotheses. Our formulation is general and fully differentiable, thus enabling to exploit the additional sparse inputs in pre-trained deep stereo networks as well as for training a new instance from scratch. Extensive experiments on three standard datasets and two state-of-the-art deep architectures show that even with a small set of sparse input cues, i) the proposed paradigm enables significant improvements to pre-trained networks. Moreover, ii) training from scratch notably increases accuracy and robustness to domain shifts. Finally, iii) it is suited and effective even with traditional stereo algorithms such as SGM.