Papers › MobileStereoNet: Towards Lightweight Deep Networks for Stereo Matching
MobileStereoNet: Towards Lightweight Deep Networks for Stereo Matching
Faranak Shamsafar, Samuel Woerz, Rafia Rahim, Andreas Zell
Recent methods in stereo matching have continuously improved the accuracy using deep models. This gain, however, is attained with a high increase in computation cost, such that the network may not fit even on a moderate GPU. This issue raises problems when the model needs to be deployed on resource-limited devices. For this, we propose two light models for stereo vision with reduced complexity and without sacrificing accuracy. Depending on the dimension of cost volume, we design a 2D and a 3D model with encoder-decoders built from 2D and 3D convolutions, respectively. To this end, we leverage 2D MobileNet blocks and extend them to 3D for stereo vision application. Besides, a new cost volume is proposed to boost the accuracy of the 2D model, making it performing close to 3D networks. Experiments show that the proposed 2D/3D networks effectively reduce the computational expense (27%/95% and 72%/38% fewer parameters/operations in 2D and 3D models, respectively) while upholding the accuracy. Our code is available at https://github.com/cogsys-tuebingen/mobilestereonet.
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Code
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Code Syntology ran Syntology
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Tasks
1 archive task tag without a task page not shown.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Stereo Depth Estimation | KITTI2015 | 3D-MobileStereoNet | three pixel error | 1.69 | #2 of 7 | Archive leaderboard | report |
| Stereo Depth Estimation | KITTI2015 | 2D-MobileStereoNet | three pixel error | 2.67 | #5 of 7 | Archive leaderboard | report |
| Stereo Depth Estimation | sceneflow | 3D-MobileStereoNet | Average End-Point Error | 0.80 | #1 of 3 | Archive leaderboard | report |
| Stereo Depth Estimation | sceneflow | 3D-MobileStereoNet | EPE | 0.80 | #1 of 3 | Archive leaderboard | report |
| Stereo Depth Estimation | sceneflow | 2D-MobileStereoNet | Average End-Point Error | 1.14 | #3 of 3 | Archive leaderboard | report |
| Stereo Depth Estimation | sceneflow | 2D-MobileStereoNet | EPE | 1.14 | #3 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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