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REVIEW 3 major objections 6 minor 46 references

SemStereo: Semantic-Constrained Stereo Matching Network for Remote Sensing

T0 review · 3 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read SemStereo imposes semantic constraints on stereo matching in three linked ways and reports state-of-the-art accuracy for both tasks on the US3D and WHU remote sensing datasets.

desk verdict Solid multi-task architecture with clean ablations, but the semantic-segmentation SOTA claim is invalid as stated: the full model uses stereo supervision and the semantic-only variant falls below S2Net. read the letter →

arxiv 2412.12685 v1 pith:JFQNTE3I submitted 2024-12-17 cs.CV

Chen Chen , Liangjin Zhao , Yuanchun He , Yingxuan Long , Kaiqiang Chen , Zhirui Wang , Yanfeng Hu , Xian Sun This is my paper · ORCID
classification cs.CV
keywords semanticsegmentationstereomatchingremotesensingmulti-tasklearningsemantic-guidedcascadedisparityrefinementleft-rightconsistencysatellite
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that semantic segmentation and stereo matching are not separate tasks in remote sensing: disparities for pixels of the same semantic category cluster in narrow ranges, so the two tasks can constrain each other. To exploit this, it proposes SemStereo, which feeds deep semantic features into the stereo branch, refines the initial disparity with semantic probability maps, and adds a left-right semantic consistency loss. On the US3D satellite dataset, the full model reduces D1 error from the baseline's 7.28% to 4.58% while raising semantic mIoU from 75.84% to 77.02%; on WHU, where no semantic labels exist, the self-supervised variant still reports the best stereo numbers among the compared methods. A reader should care because improving either task is useful, and the paper claims both improve together without needing semantic labels at inference.

What carries the argument

The load-bearing structures are three proposed components. The Semantic-Guided Cascade (SGC) replaces the usual parallel two-branch multi-task design with a cascade that sends deep, semantically enriched features into the Fast-ACV cost volume, an attention-weighted concatenation volume inherited from prior stereo work, used for the initial disparity map. The Semantic Selective Refinement (SSR) module computes a class-probability-weighted feature volume and learns per-pixel disparity residuals from it. The Left-Right Semantic Consistency (LRSC) supervision warps the left-view semantic map, ground truth if available and otherwise the predicted map, to the right view using the refined disparity and applies a cross-entropy loss against the right-view prediction.

What would settle it

Retrain every stereo baseline in Table 2 on the paper's exact 1,500/139/500 Jacksonville split and recompute EPE and D1; if Fast-ACVNet or IGEV-Stereo matches or beats SemStereo's 0.9582 EPE / 4.58% D1, the state-of-the-art claim is contradicted. Running the published code on the original US3D benchmark split, if one exists, would also settle whether the custom split is the source of the gains.

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Extended reading notes

Core claim

The paper's central discovery is that explicit semantic constraints improve stereo matching in remote sensing, and that stereo supervision in turn sharpens segmentation, yielding a state-of-the-art joint solver. The mechanism is a cascade: a shared U-shaped MobileViTv2 encoder produces deep features that feed both a segmentation head and a Fast-ACV cost volume for the initial disparity. A Semantic Selective Refinement branch multiplies class probability maps with a joint feature volume to learn disparity residuals, and a Left-Right Semantic Consistency loss warps the left semantic map to the right view through the refined disparity and penalizes divergence. The authors report that this outperforms prior stereo and semantic-segmentation baselines on US3D and WHU and that each added constraint contributes in ablations.

Load-bearing premise

The head-to-head state-of-the-art claim assumes that the published baseline results in Table 2 were obtained on the same train/validation/test partition as the paper's custom random split of US3D, since the baselines are not retrained on that split.

Editorial extensions

If this is right

  • If the reported results hold, deploying SemStereo on stereo satellite imagery yields both a disparity map and a semantic map from one forward pass, with the stereo task measurably better than using the stereo network alone.
  • Semantic labels are only needed during training; at inference the full model takes an image pair and produces both outputs with no external semantic input.
  • When semantic labels are unavailable, the LRSC module can run with predicted semantic maps, so the stereo improvement transfers to datasets such as WHU that lack semantic annotation.
  • The authors' finding that disparities concentrate by semantic category implies that category-level behavior, not just architecture, is what couples the tasks in overhead imagery.
  • With 500 fine-tuning pairs from a new city, the model's D1 on Omaha (4.54%) approaches its Jacksonville test result (4.58%), suggesting the learned semantic-disparity coupling transfers across cities.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A natural extension the paper leaves implicit is to replace category-level constraints with instance-level ones: since semantic instances appear tied to disparity more tightly than categories, an instance-aware weighting could sharpen boundaries on small objects such as the signal tower shown in the qualitative results.
  • The LRSC warping loss could double as a cross-city self-training recipe: unlabeled stereo pairs from a new city could train the segmentation head by warping predicted left-view maps to the right view, without any semantic labels.
  • The paper's reported mIoU gain from stereo supervision (67.57% to 77.02% with disparity labels) suggests stereo matching acts as a geometric regularizer for segmentation; a testable consequence is that corrupting or shuffling disparity ground truth during training should reduce semantic accuracy if the coupling is causal.
  • The method's reliance on a fixed disparity range per dataset means its benefit on very-high-resolution or multi-view imagery, where disparities span hundreds of pixels, is untested; applying SGC, SSR, and LRSC with a coarse-to-fine search would be the natural next experiment.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The paper proposes SemStereo, a joint semantic segmentation and stereo matching network for remote sensing imagery. It introduces three components: a Semantic-Guided Cascade (SGC) structure that feeds deep semantic features into the cost-volume stage, a Semantic Selective Refinement (SSR) module that refines the initial disparity under the guidance of the predicted semantic map, and a Left-Right Semantic Consistency (LRSC) loss that warps the left-view semantic map to the right view using the predicted disparity. Experiments on US3D and WHU report improved stereo matching and semantic segmentation accuracy over several baselines, with ablations showing each module contributes. The code is publicly released.

Significance. If the reported results hold, SemStereo offers a coherent and interpretable way to couple semantic and geometric tasks in remote sensing, where semantic categories have distinctive disparity distributions (Figure 2). The release of code is a practical strength, and the internal ablations are consistent, showing monotonic gains from SGC, SSR, and LRSC. The observation about intra-class disparity concentration is a falsifiable empirical claim that could inform future designs. However, the state-of-the-art claims are partly overreaching: the semantic segmentation comparison is unfair because the full model uses stereo ground truth that single-task baselines lack, and the stereo comparison relies on a custom split without re-running baselines. The self-supervised variant of LRSC also has a potential trivial-consistency risk. These issues are addressable but currently weaken the central claims.

major comments (3)
  1. [Experiments, Semantic Segmentation (Table 4)] The abstract and conclusion claim state-of-the-art semantic segmentation, but Table 4 compares the full SemStereo (trained with stereo disparity ground truth and the LRSC loss) against single-task semantic segmentation networks (FCN-8s, UNet, DeepLabV3, PSPNet, SegFormer) that receive only semantic labels. The paper's own numbers show SemStereo* without stereo supervision reaches 67.57 mIoU, below S2Net's 69.10, while the full model's 77.02 mIoU includes a 9.45-point gain from stereo supervision. This is not a like-for-like comparison. The semantic segmentation SOTA claim should be either restricted to SemStereo* or the authors should compare against multi-task baselines trained with the same disparity labels.
  2. [Experiments, Datasets and Table 2] The US3D stereo results are obtained on a custom random split (1,500 train / 139 validation / 500 test pairs from Jacksonville), but the baseline numbers in Table 2 appear to be taken from prior publications that may use different train/test partitions. Without re-running the baselines on the same split or at least documenting the split used by each cited result, the reported margins (e.g., D1 4.58% vs. 6.87% for PSMNet) and the state-of-the-art label are not established. The paper also reports a single run without error bars or repeated seeds, which is important given the small validation set.
  3. [Left-Right Semantic Consistency Supervision, Eq. (5)] In the self-supervised mode (no semantic labels), Eq. (5) warps the model's own predicted semantic map P^l using the model's own refined disparity d_final to supervise P^r. This creates a potential trivial-consistency loop: the LRSC loss can be minimized by predicting the same semantic map for both views or by collapsing disparity, without improving genuine cross-view semantic consistency. The paper reports that SemStereo* improves over Baseline* but does not analyze this risk. I recommend adding an analysis of the learned P^r against ground-truth right-view labels (where available) or comparing against a variant that warps a detached or frozen semantic map instead of the trainable P^l.
minor comments (6)
  1. [Eq. (1) and Eq. (2)] The symbol σ is used for two different weight maps in Eqs. (1)-(2); please use distinct notation (e.g., σ1 and σ2) to avoid confusion.
  2. [Eq. (3) and surrounding text] The typesetting of d_final is inconsistent ('df inal' in the text and 'df inal' in Eq. (3)); please fix the spacing and ensure the formula is readable.
  3. [Table 2] The full SemStereo is not evaluated on WHU; consider reporting it or explaining why only the downgraded SemStereo* is shown for that dataset.
  4. [Ablation Study] The percentage improvements (e.g., 'improves the D1 metric by 3.8%') are relative improvements; please state this explicitly to avoid ambiguity with absolute differences.
  5. [Figure 2] It is unclear how the disparity-per-semantic-class distribution is computed; please specify the dataset and whether ground-truth or estimated disparity is used.
  6. [Implementation Details] The phrase 'we train each stage for 48 epochs' is vague; clarify whether this means the entire multi-task network or separate training phases for the semantic and stereo branches.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: central claims rest on external benchmark experiments, not on equations that contain their own conclusions.

full rationale

SemStereo's contributions are architectural and loss-based, and its performance claims are established empirically on the external US3D and WHU benchmarks rather than derived from equations that presuppose the reported improvements. The joint loss (Eq. 9) combines a disparity loss, a semantic segmentation loss, and a cross-view semantic consistency loss; the latter (Eqs. 5-6) warps semantic labels using the model's own disparity estimate, which is a form of self-supervision but does not by construction force the measured D1/EPE/mIoU numbers. No fitted parameter is renamed as a prediction, and no result is imported from a same-author citation as a load-bearing uniqueness argument. The ablation study compares the proposed modules against the same framework with components removed, which is a standard internal validation. Concerns raised by the skeptical reading concern the comparability of the semantic-segmentation SOTA claim (the full model receives stereo supervision that single-task baselines lack) and the use of a custom US3D split against published baseline numbers. These are validity or fairness issues, not circularity: the paper does not define its success metric in terms of the trained model's own outputs, and the benchmark numbers are external evidence. Therefore no circular step meets the required evidentiary standard, and the paper is self-contained with respect to circularity analysis.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The model's free parameters are the manually chosen loss weights. The key domain assumptions are the narrow disparity-per-category prior and the comparability of the custom US3D split to published benchmarks. No invented physical entities are introduced.

free parameters (2)
  • loss weights lambda_0 to lambda_3 = 1, 0.6, 0.5, 0.3
    Set by hand in Implementation Details; relative weighting of multi-stage disparity losses, not learned or justified.
  • loss weights alpha and beta = 1, 1
    Set to 1 for segmentation and LRSC losses; affects the balance between tasks and is not derived.
assumptions (3)
  • domain assumption Disparities corresponding to the same semantic category are concentrated in a narrow range in remote sensing imagery.
    Stated in the Introduction and illustrated in Figure 2; this motivates the SSR module's intra-class disparity consistency. If false, the semantic guidance may not transfer to disparity refinement.
  • domain assumption The custom US3D split is comparable to the splits used by the baselines cited in Table 2.
    Used implicitly when comparing all methods in Table 2. The paper randomly selects 1500/139/500 from Jacksonville but does not state that prior published numbers use the same partition.
  • standard math Warping the left-view semantic map with the predicted disparity yields a valid right-view semantic map (Eq. 5).
    This follows from epipolar geometry and assumes accurate disparity and rectified image pairs; it underpins the LRSC loss.

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Cite this review

Pith. "Pith review of SemStereo: Semantic-Constrained Stereo Matching Network for Remote Sensing." pith.science (2026). https://pith.science/paper/JFQNTE3I

@misc{pith2026241212685,
  author       = {Pith},
  title        = {Pith review of: SemStereo: Semantic-Constrained Stereo Matching Network for Remote Sensing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JFQNTE3I}},
  note         = {Machine review of arXiv:2412.12685}
}
read the original abstract

Semantic segmentation and 3D reconstruction are two fundamental tasks in remote sensing, typically treated as separate or loosely coupled tasks. Despite attempts to integrate them into a unified network, the constraints between the two heterogeneous tasks are not explicitly modeled, since the pioneering studies either utilize a loosely coupled parallel structure or engage in only implicit interactions, failing to capture the inherent connections. In this work, we explore the connections between the two tasks and propose a new network that imposes semantic constraints on the stereo matching task, both implicitly and explicitly. Implicitly, we transform the traditional parallel structure to a new cascade structure termed Semantic-Guided Cascade structure, where the deep features enriched with semantic information are utilized for the computation of initial disparity maps, enhancing semantic guidance. Explicitly, we propose a Semantic Selective Refinement (SSR) module and a Left-Right Semantic Consistency (LRSC) module. The SSR refines the initial disparity map under the guidance of the semantic map. The LRSC ensures semantic consistency between two views via reducing the semantic divergence after transforming the semantic map from one view to the other using the disparity map. Experiments on the US3D and WHU datasets demonstrate that our method achieves state-of-the-art performance for both semantic segmentation and stereo matching.

Figures

Figures reproduced from arXiv: 2412.12685 by the authors.

Figure 1
Figure 1. A comparison between our and previous methods. [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. The distribution of disparity per semantic category [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. An overview of the SemStereo. It involves (a) a Semantic-Guided Cascade (SGC) structure for generating segmenta [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Qualitative comparison of results with other state-of-the-art models on the US3D test set. Red box area: Our Sem [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Qualitative comparison of results for local details [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: Qualitative comparison of semantic results with other classic state-of-the-art models on the US3D test set. Our [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]

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Works this paper leans on

46 extracted references · 30 canonical work pages

  1. [1]

    , " * write output.state after.block = add.period write newline

    ENTRY address archivePrefix author booktitle chapter edition editor eid eprint howpublished institution isbn journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.a...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

  3. [3]

    D.; Pock, T.; and Schindler, K

    Blaha, M.; Vogel, C.; Richard, A.; Wegner, J. D.; Pock, T.; and Schindler, K. 2016. Large-scale semantic 3d reconstruction: an adaptive multi-resolution model for multi-class volumetric labeling. In Proceedings of the IEEE conference on computer vision and pattern recognition, 3176--3184

    work page 2016

  4. [4]

    D.; and Brown, M

    Bosch, M.; Foster, K.; Christie, G.; Wang, S.; Hager, G. D.; and Brown, M. 2019. Semantic Stereo for Incidental Satellite Images. In The IEEE Winter Conference on Applications of Computer Vision

    work page 2019

  5. [5]

    Chang, J.-R.; and Chen, Y.-S. 2018. Pyramid stereo matching network. In Proceedings of the IEEE conference on computer vision and pattern recognition, 5410--5418

    2018

  6. [6]

    Chen, L.-C.; Papandreou, G.; Schroff, F.; and Adam, H. 2017. Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587

    arXiv 2017

  7. [7]

    Cheng, J.; Tsai, Y.-H.; Wang, S.; and Yang, M.-H. 2017. Segflow: Joint learning for video object segmentation and optical flow. In Proceedings of the IEEE international conference on computer vision, 686--695

    work page 2017

  8. [8]

    L.; Poggi, M.; Andraghetti, L.; Mart \' , M.; Kjellstr \"o m, H.; Pieropan, A.; and Mattoccia, S

    Dovesi, P. L.; Poggi, M.; Andraghetti, L.; Mart \' , M.; Kjellstr \"o m, H.; Pieropan, A.; and Mattoccia, S. 2020. Real-time semantic stereo matching. In 2020 IEEE international conference on robotics and automation (ICRA), 10780--10787. IEEE

    work page 2020

Show all 46 references
  1. [9]

    Guo, X.; Yang, K.; Yang, W.; Wang, X.; and Li, H. 2019. Group-wise correlation stereo network. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 3273--3282

    2019

  2. [10]

    Hane, C.; Zach, C.; Cohen, A.; Angst, R.; and Pollefeys, M. 2013. Joint 3D scene reconstruction and class segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 97--104

    2013

  3. [11]

    He, S.; Li, S.; Jiang, S.; and Jiang, W. 2022. HMSM-Net: Hierarchical multi-scale matching network for disparity estimation of high-resolution satellite stereo images. ISPRS Journal of Photogrammetry and Remote Sensing, 188: 314--330

    2022

  4. [12]

    He, S.; Zhou, R.; Li, S.; Jiang, S.; and Jiang, W. 2021. Disparity estimation of high-resolution remote sensing images with dual-scale matching network. Remote Sensing, 13(24): 5050

    2021

  5. [13]

    Jing, H.; Wang, Z.; Sun, X.; Xiao, D.; and Fu, K. 2021. PSRN: Polarimetric space reconstruction network for PolSAR image semantic segmentation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 10716--10732

    2021

  6. [14]

    Kadhim, N.; Mourshed, M.; and Bray, M. 2016. Advances in remote sensing applications for urban sustainability. Euro-Mediterranean Journal for Environmental Integration, 1(1): 7

    2016

  7. [15]

    Kang, J.; Wang, Z.; Zhu, R.; Sun, X.; Fernandez-Beltran, R.; and Plaza, A. 2021. PiCoCo: Pixelwise contrast and consistency learning for semisupervised building footprint segmentation. IEEE journal of selected topics in applied earth observations and remote sensing, 14: 10548--10559

    2021

  8. [16]

    Kang, J.; Wang, Z.; Zhu, R.; Xia, J.; Sun, X.; Fernandez-Beltran, R.; and Plaza, A. 2022. DisOptNet: Distilling semantic knowledge from optical images for weather-independent building segmentation. IEEE Transactions on Geoscience and Remote Sensing, 60: 1--15

    2022

  9. [17]

    Kendall, A.; Gal, Y.; and Cipolla, R. 2018. Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In Proceedings of the IEEE conference on computer vision and pattern recognition, 7482--7491

    2018

  10. [18]

    Kendall, A.; Martirosyan, H.; Dasgupta, S.; Henry, P.; Kennedy, R.; Bachrach, A.; and Bry, A. 2017. End-to-end learning of geometry and context for deep stereo regression. In Proceedings of the IEEE international conference on computer vision, 66--75

    2017

  11. [19]

    Khamis, S.; Fanello, S.; Rhemann, C.; Kowdle, A.; Valentin, J.; and Izadi, S. 2018. Stereonet: Guided hierarchical refinement for real-time edge-aware depth prediction. In Proceedings of the European Conference on Computer Vision (ECCV), 573--590

    2018

  12. [20]

    M.; Brown, M.; Hager, G.; Yokoya, N.; et al

    Kunwar, S.; Chen, H.; Lin, M.; Zhang, H.; D’Angelo, P.; Cerra, D.; Azimi, S. M.; Brown, M.; Hager, G.; Yokoya, N.; et al. 2020. Large-scale semantic 3-D reconstruction: Outcome of the 2019 IEEE GRSS data fusion contest—Part A. IEEE Journal of Selected Topics in Applied Earth O...

    2020

  13. [21]

    Le Saux, B.; Yokoya, N.; H \"a nsch, R.; and Brown, M. 2019 a . 2019 ieee grss data fusion contest: large-scale semantic 3d reconstruction. IEEE Geoscience and Remote Sensing Magazine (GRSM), 7(4): 33--36

    2019

  14. [22]

    Le Saux, B.; Yokoya, N.; Hansch, R.; Brown, M.; and Hager, G. 2019 b . 2019 data fusion contest [technical committees]. IEEE Geoscience and Remote Sensing Magazine, 7(1): 103--105

    2019

  15. [23]

    Liao, P.; Zhang, X.; Chen, G.; Wang, T.; Li, X.; Yang, H.; Zhou, W.; He, C.; and Wang, Q. 2023. S 2 Net: A Multi-task Learning Network for Semantic Stereo of Satellite Image Pairs. IEEE Transactions on Geoscience and Remote Sensing

    2023

  16. [24]

    Liu, J.; and Ji, S. 2020. A novel recurrent encoder-decoder structure for large-scale multi-view stereo reconstruction from an open aerial dataset. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 6050--6059

    2020

  17. [25]

    Long, J.; Shelhamer, E.; and Darrell, T. 2015. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, 3431--3440

    2015

  18. [26]

    G.; and Urtasun, R

    Luo, W.; Schwing, A. G.; and Urtasun, R. 2016. Efficient deep learning for stereo matching. In Proceedings of the IEEE conference on computer vision and pattern recognition, 5695--5703

    2016

  19. [27]

    Mayer, N.; Ilg, E.; Hausser, P.; Fischer, P.; Cremers, D.; Dosovitskiy, A.; and Brox, T. 2016. A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In Proceedings of the IEEE conference on computer vision and pattern recogniti...

    2016

  20. [28]

    Mehta, S.; and Rastegari, M. 2021. Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer. arXiv preprint arXiv:2110.02178

    2021 arXiv

  21. [29]

    Mehta, S.; and Rastegari, M. 2022. Separable self-attention for mobile vision transformers. arXiv preprint arXiv:2206.02680

    2022 arXiv

  22. [30]

    Qin, R.; Huang, X.; Liu, W.; and Xiao, C. 2019. Pairwise stereo image disparity and semantics estimation with the combination of u-net and pyramid stereo matching network. In IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, 4971--4974. IEEE

    2019

  23. [31]

    Ronneberger, O.; Fischer, P.; and Brox, T. 2015. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention--MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part II...

    2015

  24. [32]

    Shen, Z.; Dai, Y.; and Rao, Z. 2021. Cfnet: Cascade and fused cost volume for robust stereo matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 13906--13915

    2021

  25. [33]

    Song, X.; Zhao, X.; Fang, L.; Hu, H.; and Yu, Y. 2020. Edgestereo: An effective multi-task learning network for stereo matching and edge detection. International Journal of Computer Vision, 128: 910--930

    2020

  26. [34]

    Sun, X.; Huang, X.; Mao, Y.; Sheng, T.; Li, J.; Wang, Z.; Lu, X.; Ma, X.; Tang, D.; and Chen, K. 2024. GABLE: A first fine-grained 3D building model of China on a national scale from very high resolution satellite imagery. Remote Sensing of Environment, 305: 114057

    2024

  27. [35]

    Wu, Z.; Wu, X.; Zhang, X.; Wang, S.; and Ju, L. 2019. Semantic stereo matching with pyramid cost volumes. In Proceedings of the IEEE/CVF international conference on computer vision, 7484--7493

    2019

  28. [36]

    M.; and Luo, P

    Xie, E.; Wang, W.; Yu, Z.; Anandkumar, A.; Alvarez, J. M.; and Luo, P. 2021. SegFormer: Simple and efficient design for semantic segmentation with transformers. Advances in Neural Information Processing Systems, 34: 12077--12090

    2021

  29. [37]

    Xu, G.; Cheng, J.; Guo, P.; and Yang, X. 2022. Attention concatenation volume for accurate and efficient stereo matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 12981--12990

    2022

  30. [38]

    Xu, G.; Wang, X.; Ding, X.; and Yang, X. 2023 a . Iterative Geometry Encoding Volume for Stereo Matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 21919--21928

    2023

  31. [39]

    Xu, G.; Wang, Y.; Cheng, J.; Tang, J.; and Yang, X. 2023 b . Accurate and efficient stereo matching via attention concatenation volume. IEEE Transactions on Pattern Analysis and Machine Intelligence

    2023

  32. [40]

    Yang, G.; Zhao, H.; Shi, J.; Deng, Z.; and Jia, J. 2018. Segstereo: Exploiting semantic information for disparity estimation. In Proceedings of the European conference on computer vision (ECCV), 636--651

    2018

  33. [41]

    Yang, Q.; Chen, G.; Tan, X.; Wang, T.; Wang, J.; and Zhang, X. 2024. S3Net: Innovating Stereo Matching and Semantic Segmentation with a Single-Branch Semantic Stereo Network in Satellite Epipolar Imagery. arXiv preprint arXiv:2401.01643

    2024 arXiv

  34. [42]

    Zbontar, J.; and LeCun, Y. 2015. Computing the stereo matching cost with a convolutional neural network. In Proceedings of the IEEE conference on computer vision and pattern recognition, 1592--1599

    2015

  35. [43]

    Zhang, F.; Prisacariu, V.; Yang, R.; and Torr, P. H. 2019 a . Ga-net: Guided aggregation net for end-to-end stereo matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 185--194

    2019

  36. [44]

    A.; Vasudevan, R.; and Johnson-Roberson, M

    Zhang, J.; Skinner, K. A.; Vasudevan, R.; and Johnson-Roberson, M. 2019 b . Dispsegnet: Leveraging semantics for end-to-end learning of disparity estimation from stereo imagery. IEEE Robotics and Automation Letters, 4(2): 1162--1169

    2019

  37. [45]

    Zhang, Y.; Chen, Y.; Bai, X.; Yu, S.; Yu, K.; Li, Z.; and Yang, K. 2020. Adaptive unimodal cost volume filtering for deep stereo matching. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, 12926--12934

    2020

  38. [46]

    Zhao, H.; Shi, J.; Qi, X.; Wang, X.; and Jia, J. 2017. Pyramid scene parsing network. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2881--2890

    2017

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