# Accurate and Efficient Stereo Matching via Attention Concatenation Volume

arXiv: https://arxiv.org/abs/2209.12699 · HTML: https://mlsanity.com/paper/2209.12699/

## Key facts

- Cited by 59 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 45 references that are in the map.
- First posted on arXiv 23 Sep 2022.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation](https://mlsanity.com/paper/1512.02134/) (2015)
- [MVSNet: Depth Inference for Unstructured Multi-view Stereo](https://mlsanity.com/paper/1804.02505/) (2018)
- [Pyramid Stereo Matching Network](https://mlsanity.com/paper/1803.08669/) (2018)
- [Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching](https://mlsanity.com/paper/1912.06378/) (2019)
- [End-to-End Learning of Geometry and Context for Deep Stereo Regression](https://mlsanity.com/paper/1703.04309/) (2017)
- [RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching](https://mlsanity.com/paper/2109.07547/) (2021)
- [Recurrent MVSNet for High-resolution Multi-view Stereo Depth Inference](https://mlsanity.com/paper/1902.10556/) (2019)
- [GA-Net: Guided Aggregation Net for End-to-end Stereo Matching](https://mlsanity.com/paper/1904.06587/) (2019)
- [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)
- [Hierarchical Neural Architecture Search for Deep Stereo Matching](https://mlsanity.com/paper/2010.13501/) (2020)
- [CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching](https://mlsanity.com/paper/2104.04314/) (2021)
- [Deep Stereo using Adaptive Thin Volume Representation with Uncertainty Awareness](https://mlsanity.com/paper/1911.12012/) (2019)
- [Learning Depth with Convolutional Spatial Propagation Network](https://mlsanity.com/paper/1810.02695/) (2018)
- [Attention Concatenation Volume for Accurate and Efficient Stereo Matching](https://mlsanity.com/paper/2203.02146/) (2022)
- [HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching](https://mlsanity.com/paper/2007.12140/) (2020)
- [DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch](https://mlsanity.com/paper/1909.05845/) (2019)
- [Superpixel Segmentation with Fully Convolutional Networks](https://mlsanity.com/paper/2003.12929/) (2020)
- [Adaptive Unimodal Cost Volume Filtering for Deep Stereo Matching](https://mlsanity.com/paper/1909.03751/) (2019)
- [Correlate-and-Excite: Real-Time Stereo Matching via Guided Cost Volume Excitation](https://mlsanity.com/paper/2108.05773/) (2021)
- [Bilateral Grid Learning for Stereo Matching Networks](https://mlsanity.com/paper/2101.01601/) (2021)
- [Parallax Attention for Unsupervised Stereo Correspondence Learning](https://mlsanity.com/paper/2009.08250/) (2020)
- [A Decomposition Model for Stereo Matching](https://mlsanity.com/paper/2104.07516/) (2021)

## Built on by (most cited first, 30 of 59)

- [Iterative Geometry Encoding Volume for Stereo Matching](https://mlsanity.com/paper/2303.06615/) (2023)
- [Selective-Stereo: Adaptive Frequency Information Selection for Stereo Matching](https://mlsanity.com/paper/2403.00486/) (2024)
- [Prompting Depth Anything for 4K Resolution Accurate Metric Depth Estimation](https://mlsanity.com/paper/2412.14015/) (2024)
- [IGEV++: Iterative Multi-range Geometry Encoding Volumes for Stereo Matching](https://mlsanity.com/paper/2409.00638/) (2024)
- [Pixel-Perfect Depth with Semantics-Prompted Diffusion Transformers](https://mlsanity.com/paper/2510.07316/) (2025)
- [Uncertainty Guided Adaptive Warping for Robust and Efficient Stereo Matching](https://mlsanity.com/paper/2307.14071/) (2023)
- [HDRFlow: Real-Time HDR Video Reconstruction with Large Motions](https://mlsanity.com/paper/2403.03447/) (2024)
- [Adaptive Fusion of Single-View and Multi-View Depth for Autonomous Driving](https://mlsanity.com/paper/2403.07535/) (2024)
- [LightStereo: Channel Boost Is All You Need for Efficient 2D Cost Aggregation](https://mlsanity.com/paper/2406.19833/) (2024)
- [Match-Stereo-Videos: Bidirectional Alignment for Consistent Dynamic Stereo Matching](https://mlsanity.com/paper/2403.10755/) (2024)
- [Playing to Vision Foundation Model's Strengths in Stereo Matching](https://mlsanity.com/paper/2404.06261/) (2024)
- [MonSter++: Unified Stereo Matching, Multi-view Stereo, and Real-time Stereo with Monodepth Priors](https://mlsanity.com/paper/2501.08643/) (2025)
- [CGI-Stereo: Accurate and Real-Time Stereo Matching via Context and Geometry Interaction](https://mlsanity.com/paper/2301.02789/) (2023)
- [DiffuVolume: Diffusion Model for Volume based Stereo Matching](https://mlsanity.com/paper/2308.15989/) (2023)
- [BANet: Bilateral Aggregation Network for Mobile Stereo Matching](https://mlsanity.com/paper/2503.03259/) (2025)
- [Match Stereo Videos via Bidirectional Alignment](https://mlsanity.com/paper/2409.20283/) (2024)
- [ADFactory: An Effective Framework for Generalizing Optical Flow with Nerf](https://mlsanity.com/paper/2311.04246/) (2023)
- [Memory-Efficient Optical Flow via Radius-Distribution Orthogonal Cost Volume](https://mlsanity.com/paper/2312.03790/) (2023)
- [FIReStereo: Forest InfraRed Stereo Dataset for UAS Depth Perception in Visually Degraded Environments](https://mlsanity.com/paper/2409.07715/) (2024)
- [Stereo Any Video: Temporally Consistent Stereo Matching](https://mlsanity.com/paper/2503.05549/) (2025)
- [Dusk Till Dawn: Self-supervised Nighttime Stereo Depth Estimation using Visual Foundation Models](https://mlsanity.com/paper/2405.11158/) (2024)
- [Hybrid Cost Volume for Memory-Efficient Optical Flow](https://mlsanity.com/paper/2409.04243/) (2024)
- [Cook and Clean Together: Teaching Embodied Agents for Parallel Task Execution](https://mlsanity.com/paper/2511.19430/) (2025)
- [FlowMamba: Learning Point Cloud Scene Flow with Global Motion Propagation](https://mlsanity.com/paper/2412.17366/) (2024)
- [Rethinking Iterative Stereo Matching from Diffusion Bridge Model Perspective](https://mlsanity.com/paper/2404.09051/) (2024)
- [Consistency-aware Self-Training for Iterative-based Stereo Matching](https://mlsanity.com/paper/2503.23747/) (2025)
- [Global Regulation and Excitation via Attention Tuning for Stereo Matching](https://mlsanity.com/paper/2509.15891/) (2025)
- [Lite Any Stereo: Efficient Zero-Shot Stereo Matching](https://mlsanity.com/paper/2511.16555/) (2025)
- [DCVSMNet: Double Cost Volume Stereo Matching Network](https://mlsanity.com/paper/2402.16473/) (2024)
- [Self-Assessed Generation: Trustworthy Label Generation for Optical Flow and Stereo Matching in Real-world](https://mlsanity.com/paper/2410.10453/) (2024)

## Abstract (arXiv)

Stereo matching is a fundamental building block for many vision and robotics applications. An informative and concise cost volume representation is vital for stereo matching of high accuracy and efficiency. In this paper, we present a novel cost volume construction method, named attention concatenation volume (ACV), which generates attention weights from correlation clues to suppress redundant information and enhance matching-related information in the concatenation volume. The ACV can be seamlessly embedded into most stereo matching networks, the resulting networks can use a more lightweight aggregation network and meanwhile achieve higher accuracy. We further design a fast version of ACV to enable real-time performance, named Fast-ACV, which generates high likelihood disparity hypotheses and the corresponding attention weights from low-resolution correlation clues to significantly reduce computational and memory cost and meanwhile maintain a satisfactory accuracy. The core idea of our Fast-ACV is volume attention propagation (VAP) which can automatically select accurate correlation values from an upsampled correlation volume and propagate these accurate values to the surroundings pixels with ambiguous correlation clues. Furthermore, we design a highly accurate network ACVNet and a real-time network Fast-ACVNet based on our ACV and Fast-ACV respectively, which achieve the state-of-the-art performance on several benchmarks (i.e., our ACVNet ranks the 2nd on KITTI 2015 and Scene Flow, and the 3rd on KITTI 2012 and ETH3D among all the published methods; our Fast-ACVNet outperforms almost all state-of-the-art real-time methods on Scene Flow, KITTI 2012 and 2015 and meanwhile has better generalization ability)