# FADNet: A Fast and Accurate Network for Disparity Estimation

arXiv: https://arxiv.org/abs/2003.10758 · HTML: https://mlsanity.com/paper/2003.10758/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2020.

## Built on by (most cited first, 30 of 31)

- [Bilateral Grid Learning for Stereo Matching Networks](https://mlsanity.com/paper/2101.01601/) (2021)
- [YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection](https://mlsanity.com/paper/2103.09422/) (2021)
- [OpenStereo: A Comprehensive Benchmark for Stereo Matching and Strong Baseline](https://mlsanity.com/paper/2312.00343/) (2023)
- [LightStereo: Channel Boost Is All You Need for Efficient 2D Cost Aggregation](https://mlsanity.com/paper/2406.19833/) (2024)
- [A Survey on Deep Stereo Matching in the Twenties](https://mlsanity.com/paper/2407.07816/) (2024)
- [Global Occlusion-Aware Transformer for Robust Stereo Matching](https://mlsanity.com/paper/2312.14650/) (2023)
- [CGI-Stereo: Accurate and Real-Time Stereo Matching via Context and Geometry Interaction](https://mlsanity.com/paper/2301.02789/) (2023)
- [One at a Time: Progressive Multi-step Volumetric Probability Learning for Reliable 3D Scene Perception](https://mlsanity.com/paper/2306.12681/) (2023)
- [BANet: Bilateral Aggregation Network for Mobile Stereo Matching](https://mlsanity.com/paper/2503.03259/) (2025)
- [FADNet++: Real-Time and Accurate Disparity Estimation with Configurable Networks](https://mlsanity.com/paper/2110.02582/) (2021)
- [ResDepth: A Deep Residual Prior For 3D Reconstruction From High-resolution Satellite Images](https://mlsanity.com/paper/2106.08107/) (2021)
- [ESGN: Efficient Stereo Geometry Network for Fast 3D Object Detection](https://mlsanity.com/paper/2111.14055/) (2021)
- [Adjacent-view Transformers for Supervised Surround-view Depth Estimation](https://mlsanity.com/paper/2303.07759/) (2023)
- [ORStereo: Occlusion-Aware Recurrent Stereo Matching for 4K-Resolution Images](https://mlsanity.com/paper/2103.07798/) (2021)
- [Digging Into Normal Incorporated Stereo Matching](https://mlsanity.com/paper/2402.18171/) (2024)
- [Lite Any Stereo: Efficient Zero-Shot Stereo Matching](https://mlsanity.com/paper/2511.16555/) (2025)
- [FastRSR: Efficient and Accurate Road Surface Reconstruction from Bird's Eye View](https://mlsanity.com/paper/2504.09535/) (2025)
- [StereoCarla: A High-Fidelity Driving Dataset for Generalizable Stereo](https://mlsanity.com/paper/2509.12683/) (2025)
- [StereoPolicy: Improving Robotic Manipulation Policies via Stereo Perception](https://mlsanity.com/paper/2605.09989/) (2026)
- [EASNet: Searching Elastic and Accurate Network Architecture for Stereo Matching](https://mlsanity.com/paper/2207.09796/) (2022)
- [TIDE: Temporally Incremental Disparity Estimation via Pattern Flow in Structured Light System](https://mlsanity.com/paper/2310.08932/) (2023)
- [SRH-Net: Stacked Recurrent Hourglass Network for Stereo Matching](https://mlsanity.com/paper/2105.11587/) (2021)
- [Iterative Volume Fusion for Asymmetric Stereo Matching](https://mlsanity.com/paper/2508.09543/) (2025)
- [Humanoid-OmniOcc: Stereo-Based Full-View Occupancy Dataset for Embodied AI](https://mlsanity.com/paper/2606.22971/) (2026)
- [Multi-Scale Cost Volumes Cascade Network for Stereo Matching](https://mlsanity.com/paper/2102.01940/) (2021)
- [ES-Net: An Efficient Stereo Matching Network](https://mlsanity.com/paper/2103.03922/) (2021)
- [Dense Depth Estimation from Multiple 360-degree Images Using Virtual Depth](https://mlsanity.com/paper/2112.14931/) (2021)
- [Scalable Vision-Based 3D Object Detection and Monocular Depth Estimation for Autonomous Driving](https://mlsanity.com/paper/2403.02037/) (2024)
- [Generalized Geometry Encoding Volume for Real-time Stereo Matching](https://mlsanity.com/paper/2512.06793/) (2025)
- [Lite Any Stereo V2: Faster and Stronger Efficient Zero-Shot Stereo Matching](https://mlsanity.com/paper/2606.24457/) (2026)

## Abstract (arXiv)

Deep neural networks (DNNs) have achieved great success in the area of computer vision. The disparity estimation problem tends to be addressed by DNNs which achieve much better prediction accuracy in stereo matching than traditional hand-crafted feature based methods. On one hand, however, the designed DNNs require significant memory and computation resources to accurately predict the disparity, especially for those 3D convolution based networks, which makes it difficult for deployment in real-time applications. On the other hand, existing computation-efficient networks lack expression capability in large-scale datasets so that they cannot make an accurate prediction in many scenarios. To this end, we propose an efficient and accurate deep network for disparity estimation named FADNet with three main features: 1) It exploits efficient 2D based correlation layers with stacked blocks to preserve fast computation; 2) It combines the residual structures to make the deeper model easier to learn; 3) It contains multi-scale predictions so as to exploit a multi-scale weight scheduling training technique to improve the accuracy. We conduct experiments to demonstrate the effectiveness of FADNet on two popular datasets, Scene Flow and KITTI 2015. Experimental results show that FADNet achieves state-of-the-art prediction accuracy, and runs at a significant order of magnitude faster speed than existing 3D models. The codes of FADNet are available at https://github.com/HKBU-HPML/FADNet.