Coarse-to-fine stereo matching enables efficient disparity estimation but remains sensitive to errors in propagated disparity, which may place ground-truth correspondences outside subsequent local search ranges. We propose TRUST-Stereo (Teacher-Assisted Robust Uncertainty-Guided Search and Transfer for Fast Stereo Matching), which addresses this limitation through an Uncertainty-Guided Residual Search Module (UGRSM). UGRSM estimates the reliability of propagated disparity and predicts residual offsets to adaptively relocate local cost-volume centers. Additionally, we introduce a two-stage Senior-Junior Teacher Assistant knowledge distillation strategy to transfer rich monocular representations from a large foundation model to a lightweight backbone to achieve fast stereo matching performance. Experiments on KITTI 2012, KITTI 2015, ETH3D, and Middlebury demonstrate strong zero-shot generalization. Using only 1.4M synthetic stereo pairs, TRUST-Stereo achieves state-of-the-art results on several benchmark metrics, outperforming competing efficient methods trained with substantially larger datasets, including real-world pseudo-labeled samples. Specifically, it achieves a KITTI 2015 EPE of 0.93, ETH3D Bad 1.0 of 2.13% and Middlebury Bad 2.0 of 6.79% while maintaining inference at approximately 50 ms per stereo pair.
Figures & tables
Figure 1: Zero-shot generalization comparison under SceneFlow-only training.
Figure 2: Overview of TRUST-Stereo. A coarse-to-fine stereo matching architecture iteratively refines disparity using ConvGRUs. The proposed UGRSM adaptively adjusts local search centers, while Senior–Junior Teacher Assistant distillation provides lightweight monocular guidance for robust zero-shot generalization.
Figure 3: Overview of the proposed two-stage Senior–Junior Teacher Assistant knowledge distillation framework.
Figure 4: Architecture of the proposed Uncertainty-Guided Residual Search Module (UGRSM). The module predicts an uncertainty map and a residual search offset from the propagated disparity and image features. The uncertainty score modulates the residual offset to produce an adjusted local cost-volume center used for local disparity search.
Method
KITTI 2012
KITTI 2015
ETH3D
Middlebury
Runtime (ms)
D1
EPE
D1
EPE
Bad 1.0
Bad 2.0
Efficient methods: SceneFlow training
CoEX [ 1 ]
22.30
3.37
17.33
3.00
31.97
26.42
27
MobileStereoNet-2D [ 36 ]
19.30
2.51
21.88
2.85
17.10
37.98
41
MobileStereoNet-3D [ 36 ]
19.59
2.67
17.89
3.01
18.42
25.26
67
FastACV+ [ 51 ]
16.69
2.29
15.28
3.27
11.50
27.34
45
Table 1: Zero-shot generalization on KITTI 2012, KITTI 2015, ETH3D, and Middlebury-H. Baseline results are adopted from [ 22 ] , except Fast Foundation Stereo R , which we retrained on SceneFlow since the official SceneFlow-only weights are not released. ETH3D and Middlebury-H results are evaluated on valid non-occluded pixels. SceneFlow-only methods are trained on SceneFlow. In million-scale training, ∗ denotes models retrained using the synthetic data and training strategy of [ 18 ] ; † denotes 30M pseudo-labeled samples; ‡ denotes 1.8M synthetic and 0.5M real-world pseudo-labeled samples; and \lx@sectionsign denotes our 1.4M synthetic-only training. Best and second-best results within each setting are highlighted in red (bold) and yellow, respectively.
Method
KITTI 2012
KITTI 2015
MACs (G)
3-noc
3-all
4-noc
4-all
EPE-noc / all
D1-bg
D1-fg
D1-all
DispNetC [ 28 ]
4.11
4.65
2.77
3.20
0.9 / 1.0
4.32
4.41
4.34
–
DeepPruner-Fast [ 11 ]
–
–
–
–
–
2.32
3.91
2.59
194
AANet+ [ 52 ]
1.55
2.04
1.20
1.58
0.4 / 0.5
1.65
3.96
2.03
–
DecNet [ 55 ]
–
–
–
–
–
2.07
3.87
2.37
–
BGNet+ [ 48 ]
1.62
2.03
1.16
1.48
0.5 / 0.6
1.81
4.09
2.19
77
Table 2: Fine-tuned comparison on the KITTI 2012 and KITTI 2015 test sets. Lower values indicate better performance. Baseline MACs are adopted from [ 22 ] and reported at 1242×375 input resolution.
Figure 5: Qualitative zero-shot disparity estimation comparison on ETH3D and Middlebury-H. The first two rows show ETH3D, while the last two rows show Middlebury-H. Disparity predictions from TRUST-Stereo (ours), Fast-FoundationStereo, LightStereo-M, and BANet-2D are compared. All models are trained exclusively on SceneFlow and evaluated zero-shot without target-domain fine-tuning.
Dataset
w/o UGRSM (%)
w/ UGRSM (%)
KITTI 2012
0.2833
0.2701
KITTI 2015
0.5189
0.3188
ETH3D
2.7000
1.4300
Middlebury
4.0777
2.6741
Table 3: Effect of UGRSM on local cost-volume coverage under SceneFlow-only zero-shot generalization. Values indicate the percentage of ground-truth correspondences outside the local search range. Lower is better.
Dataset
Metric
w/o UGRSM
UGRSM (No Uncertainty)
Full UGRSM (Ours)
KITTI 2012
D1 (%)
4.81
4.85
4.64
KITTI 2015
D1 (%)
6.79
6.73
6.23
ETH3D
Bad 1.0 (%)
9.62
13.46
9.12
Middlebury
Bad 2.0 (%)
15.96
14.82
14.62
Table 4: Effect of uncertainty-guided residual search on zero-shot disparity accuracy. All variants are trained on SceneFlow without knowledge distillation. ETH3D and Middlebury are evaluated on all valid pixels, including occluded regions. Lower is better.
Dataset
Metric
Direct KD
ViTKD
SJTA KD (Ours)
KITTI 2012
EPE
0.95
0.99
0.93
D1 (%)
4.45
4.96
4.30
KITTI 2015
EPE
1.19
1.18
1.16
D1 (%)
5.71
6.35
5.78
ETH3D
Bad 1.0 (%)
24.80
21.31
8.79
Bad 2.0 (%)
20.43
16.55
4.35
Table 5: Zero-shot comparison of direct Teacher–Student KD, ViTKD, and the proposed Senior–Junior TA KD. Lower is better.
SJTA
UGRSM
KITTI 2012
KITTI 2015
D1 (%) ↓
D1 (%) ↓
×
×
4.81
6.79
×
✓
4.64
6.23
✓
×
4.60
6.43
✓
✓
4.30
5.78
Table 6: Ablation study of the proposed Senior–Junior Teacher Assistant (SJTA) distillation and Uncertainty-Guided Residual Search Module (UGRSM) under SceneFlow-only training. All variants are evaluated zero-shot on KITTI 2012 and KITTI 2015 using D1 (%). Lower is better.
School of Control Science and Engineering, Dalian University of Technology, Dalian, 116024, China · Pika Labs · Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China +1