# Continuous 3D Label Stereo Matching using Local Expansion Moves

arXiv: https://arxiv.org/abs/1603.08328 · HTML: https://mlsanity.com/paper/1603.08328/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2016.

## Built on by (most cited first, 16 of 16)

- [RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching](https://mlsanity.com/paper/2109.07547/) (2021)
- [Practical Stereo Matching via Cascaded Recurrent Network with Adaptive Correlation](https://mlsanity.com/paper/2203.11483/) (2022)
- [Neural Markov Random Field for Stereo Matching](https://mlsanity.com/paper/2403.11193/) (2024)
- [Global Occlusion-Aware Transformer for Robust Stereo Matching](https://mlsanity.com/paper/2312.14650/) (2023)
- [RIAV-MVS: Recurrent-Indexing an Asymmetric Volume for Multi-View Stereo](https://mlsanity.com/paper/2205.14320/) (2022)
- [ORStereo: Occlusion-Aware Recurrent Stereo Matching for 4K-Resolution Images](https://mlsanity.com/paper/2103.07798/) (2021)
- [DeepPS2: Revisiting Photometric Stereo Using Two Differently Illuminated Images](https://mlsanity.com/paper/2207.02025/) (2022)
- [Lite Any Stereo: Efficient Zero-Shot Stereo Matching](https://mlsanity.com/paper/2511.16555/) (2025)
- [Quantum-Hybrid Stereo Matching With Nonlinear Regularization and Spatial Pyramids](https://mlsanity.com/paper/2312.16118/) (2023)
- [An evaluation of Deep Learning based stereo dense matching dataset shift from aerial images and a large scale stereo dataset](https://mlsanity.com/paper/2402.12522/) (2024)
- [Stereo Matching with Cost Volume based Sparse Disparity Propagation](https://mlsanity.com/paper/2201.11937/) (2022)
- [Analysis of different disparity estimation techniques on aerial stereo image datasets](https://mlsanity.com/paper/2410.06711/) (2024)
- [RGB-Phase Speckle: Cross-Scene Stereo 3D Reconstruction via Wrapped Pre-Normalization](https://mlsanity.com/paper/2503.06125/) (2025)
- [MatchAttention: Embedding Explicit Matching Constraints into Attention for Efficient Stereo Matching](https://mlsanity.com/paper/2510.14260/) (2025)
- [Velocity and stroke rate reconstruction of canoe sprint team boats based on panned and zoomed video recordings](https://mlsanity.com/paper/2602.22941/) (2026)
- [Lite Any Stereo V2: Faster and Stronger Efficient Zero-Shot Stereo Matching](https://mlsanity.com/paper/2606.24457/) (2026)

## Abstract (arXiv)

We present an accurate stereo matching method using local expansion moves based on graph cuts. This new move-making scheme is used to efficiently infer per-pixel 3D plane labels on a pairwise Markov random field (MRF) that effectively combines recently proposed slanted patch matching and curvature regularization terms. The local expansion moves are presented as many alpha-expansions defined for small grid regions. The local expansion moves extend traditional expansion moves by two ways: localization and spatial propagation. By localization, we use different candidate alpha-labels according to the locations of local alpha-expansions. By spatial propagation, we design our local alpha-expansions to propagate currently assigned labels for nearby regions. With this localization and spatial propagation, our method can efficiently infer MRF models with a continuous label space using randomized search. Our method has several advantages over previous approaches that are based on fusion moves or belief propagation; it produces submodular moves deriving a subproblem optimality; it helps find good, smooth, piecewise linear disparity maps; it is suitable for parallelization; it can use cost-volume filtering techniques for accelerating the matching cost computations. Even using a simple pairwise MRF, our method is shown to have best performance in the Middlebury stereo benchmark V2 and V3.