# Stereo Matching with Cost Volume based Sparse Disparity Propagation

arXiv: https://arxiv.org/abs/2201.11937 · HTML: https://mlsanity.com/paper/2201.11937/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 59 references that are in the map.
- First posted on arXiv 28 Jan 2022.

## Builds on (most cited first, 14 of 14)

- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation](https://mlsanity.com/paper/1512.02134/) (2015)
- [End-to-End Learning of Geometry and Context for Deep Stereo Regression](https://mlsanity.com/paper/1703.04309/) (2017)
- [Group-wise Correlation Stereo Network](https://mlsanity.com/paper/1903.04025/) (2019)
- [AANet: Adaptive Aggregation Network for Efficient Stereo Matching](https://mlsanity.com/paper/2004.09548/) (2020)
- [CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching](https://mlsanity.com/paper/2104.04314/) (2021)
- [Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches](https://mlsanity.com/paper/1510.05970/) (2015)
- [Intel RealSense Stereoscopic Depth Cameras](https://mlsanity.com/paper/1705.05548/) (2017)
- [MirrorFlow: Exploiting Symmetries in Joint Optical Flow and Occlusion Estimation](https://mlsanity.com/paper/1708.05355/) (2017)
- [Continuous 3D Label Stereo Matching using Local Expansion Moves](https://mlsanity.com/paper/1603.08328/) (2016)
- [Stereo Matching With Color-Weighted Correlation, Hierarchical Belief Propagation And Occlusion Handling](https://mlsanity.com/paper/1708.07987/) (2017)
- [CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation](https://mlsanity.com/paper/1804.01967/) (2018)
- [UDFNet: Unsupervised Disparity Fusion with Adversarial Networks](https://mlsanity.com/paper/1904.10044/) (2019)

## Built on by (most cited first, 1 of 1)

- [A Recursive Pyramidal Algorithm for Solving the Image Registration Problem](https://mlsanity.com/paper/2510.04231/) (2025)

## Abstract (arXiv)

Stereo matching is crucial for binocular stereo vision. Existing methods mainly focus on simple disparity map fusion to improve stereo matching, which require multiple dense or sparse disparity maps. In this paper, we propose a simple yet novel scheme, termed feature disparity propagation, to improve general stereo matching based on matching cost volume and sparse matching feature points. Specifically, our scheme first calculates a reliable sparse disparity map by local feature matching, and then refines the disparity map by propagating reliable disparities to neighboring pixels in the matching cost domain. In addition, considering the gradient and multi-scale information of local disparity regions, we present a ρ-Census cost measure based on the well-known AD-Census, which guarantees the robustness of cost volume even without the cost aggregation step. Extensive experiments on Middlebury stereo benchmark V3 demonstrate that our scheme achieves promising performance comparable to state-of-the-art methods.