Pith. sign in

REVIEW 3 major objections 4 minor 2 cited by

TS-SatMVSNet: Slope Aware Height Estimation for Large-Scale Earth Terrain Multi-view Stereo

T0 review · 3 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper claims that feeding slope information, computed from each stage's height map, back into a coarse-to-fine satellite MVS network improves terrain height estimation, reporting state-of-the-art MAE of 1.879 m on WHU-TLC and…

desk verdict A useful slope-guided interval partition buried under a slope direction loss that cannot backpropagate as written. read the letter →

arxiv 2501.01049 v1 pith:YMACNZKX submitted 2025-01-02 cs.CV

Song Zhang , Zhiwei Wei , Wenjia Xu , Lili Zhang , Yang Wang , Jinming Zhang , Junyi Liu This is my paper · ORCID
classification cs.CV
keywords satellitemulti-viewstereoterrainheightestimationslope-awareMVSRPCwarpingdigitalsurfacemodelhypothesisintervalsGaussiancorrectionWHU-TLCdataset
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

This paper argues that general-purpose learning-based multi-view stereo (MVS) pipelines for satellite imagery ignore a basic fact about the Earth's surface: terrain is gently undulating, and its local steepness can be measured by slope. The authors propose TS-SatMVSNet, an end-to-end three-stage MVS network that computes a slope map and a slope-direction map from its own predicted height map using a 3x3 max-absolute-difference heuristic, then uses those maps to reallocate per-pixel height hypothesis planes, to correct heights with a learnable Gaussian filter, and to add a slope-direction loss against a pseudo ground truth derived from the GT height map. On WHU-TLC, the method reports MAE of 1.879 m against 1.895 m for SatMVS-F and raises the fraction of pixels within 7.5 m of ground truth from 80.05% to 97.34%; on MVS3D it reports the best average <1.0 m, Median, and RMSE among compared methods. If correct, the work would show that cheap terrain geometry priors like slope can be folded into existing MVS pipelines to reduce artifacts and improve DSM completeness without extra supervision data.

What carries the argument

The Height-based Slope Calculation Strategy (HSCS) carries the argument: for each pixel $x$ with 3x3 height patch $p_{3\times 3}(x)$, it defines the slope as $S(x) = |\max(p_{3\times 3}(x)) - H(x)|$ and the slope direction as the integer 0-8 pointing from the center to the max-height cell, with code 4 meaning the center is the maximum. Because this map is computed from the network's own height predictions, no external slope labels are needed. The slope map feeds the Slope-guided Interval Partition Module, which splits each pixel's $[H-\sigma, H+\sigma]$ height range into lower and upper sub-ranges weighted by $S_{\min}$ and $S_{\max}$, placing more hypothesis planes on the steeper side. The Height Correction Module applies a 3x3 learnable Gaussian kernel, parameterized as a fixed 1/16, 1/8, 1/4 pattern scaled by a learned multiplier, to smooth abnormal height values. The slope-direction map is then compared to a pseudo ground-truth slope-direction map, computed from the GT height map, through an L2 loss.

What would settle it

To test the central mechanism, hold the pseudo ground-truth slope-direction map fixed and compute the gradient of the slope-direction loss with respect to the predicted height map. If the discrete max-and-code procedure is implemented as described, that gradient is zero almost everywhere, so a training run with the slope-direction loss removed should give identical heights to one with it enabled; if it does, the loss is inert and any gains come from the two slope-guided modules and the height loss.

Watch

Extended reading notes

Core claim

The central claim is that integrating slope information into a satellite MVS pipeline materially improves terrain height estimation. The paper's specific discovery is that a height map itself can be turned into a slope representation with no additional supervision: for each pixel, the slope is the absolute difference between the center height and the maximum of its eight neighbors, and the slope direction is the direction from the center toward that maximum, encoded as one of nine integer codes. That representation drives two novel modules, a slope-guided interval partition and a learnable Gaussian height correction, plus a slope-direction loss, allowing the coarse-to-fine stages to adapt to terrain undulation. The authors summarize the contribution as a complete slope-aware height estimation network that reports state-of-the-art accuracy on WHU-TLC and competitive generalization on MVS3D.

Load-bearing premise

The load-bearing premise is that the slope-direction loss can train the network: gradients must flow from that loss back through the discrete step that picks the tallest neighbor and assigns its direction code, and that step as written gives the predicted heights no gradient.

Editorial extensions

If this is right

  • The paper's ablations show that adding slope-guided interval partitioning to the baseline improves MAE from 2.026 m to 1.911 m on WHU-TLC; adding the height correction module alone improves MAE to 2.002 m.
  • On the full WHU-TLC test set the model reports MAE 1.879 m and RMSE 3.892 m, versus 1.895 m and 3.654 m for SatMVS-F, and <7.5 m accuracy rises to 97.34% from 80.05%.
  • Accuracy stays nearly constant across resolutions: moving from 2048x1472 to 5120x5120 changes the paper's metrics by only 0.1-0.6%, whereas SatMVS(RED-Net) fluctuates by up to 15.9%.
  • On MVS3D the WHU-TLC-pretrained model reports the best average <1.0 m (60.635%), Median (0.353 m), and RMSE (2.898 m), indicating that slope-aware pretraining transfers to a different satellite sensor without fine-tuning.
  • Because the slope maps come from the network's own height estimates rather than external labels, the same mechanism supplies training supervision and inference-time refinement without additional annotation effort.

Reading between the lines

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

  • One extension the paper leaves implicit: the one-sided max-difference slope operator could be replaced by a symmetric gradient or Sobel-style estimate, which would treat upward and downward terrain changes identically and might soften the urban artifacts the paper reports.
  • A straight-through estimator or regression to a direction vector would give the slope-direction loss a meaningful gradient through the discrete direction-encoding step; without such a mechanism, the loss cannot be assumed to train the network.
  • The same two slope-guided modules could be grafted onto other MVS or dense-stereo backbones; the paper only tests them inside its three-stage RPC pipeline, so whether they transfer across architectures remains open.
  • Given the paper's own admission that urban scenes break the small-plane assumption, a learned gate that turns slope guidance off at abrupt edges is a natural next test.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 4 minor

Summary. The paper proposes TS-SatMVSNet, a coarse-to-fine multi-stage satellite multi-view stereo network for terrain height estimation. The core idea is to compute a slope map and a slope direction map from the predicted height map using a 3x3 maximum-height-difference heuristic, then use these in two modules: a slope-guided interval partition module that reallocates height hypothesis planes and a height correction module based on a learnable Gaussian filter. The paper also introduces a slope direction loss between predicted and pseudo-ground-truth slope direction maps. Experiments are reported on WHU-TLC and MVS3D, with quantitative comparisons against traditional and learning-based methods, ablations of the proposed modules, and qualitative visualizations.

Significance. The general idea of injecting terrain-specific inductive bias into satellite MVS height estimation is timely and potentially useful for large-scale Earth surface reconstruction, and the paper provides a reasonably broad empirical study spanning two datasets and multiple comparison methods. The ablation of the slope-guided interval partition and height correction modules shows measurable improvements over the baseline, which is a useful empirical result. However, the central technical claim is undermined by the non-differentiability of the slope direction loss as described, and several quantitative claims about state-of-the-art performance are not supported by the reported numbers, so the paper in its current form does not establish the claimed contributions.

major comments (3)
  1. [Methodology, Algorithm 1 and Eq. (9)] The slope direction loss in Eq. (9) is not a valid training signal as described. Algorithm 1 computes the direction code by selecting the index of the maximum height in each 3x3 patch and then indexing p3x3 with that integer; this argmax/index operation has zero gradient almost everywhere with respect to the predicted height map. No straight-through estimator, soft-argmax, or other relaxation is mentioned. Consequently the gradient of L_s with respect to the network parameters is zero almost everywhere, and L_overall in Eq. (10) effectively reduces to the height loss. The claim that the slope direction loss 'implicitly optimizes height estimation' is unsupported, and Table 3 does not include an ablation row isolating this loss, so there is no experimental evidence for its contribution. Additionally, the direction codes 0-8 are categorical labels, so an L2 loss on them is semantically questionable even if a differentiable surrogate were introduced.
  2. [Introduction and Table 1] The state-of-the-art and quantitative improvement claims are not supported by the reported results. The Introduction states that the method exceeds other SatMVS-based methods by 'at least 16% in MAE metric and at least 5% in < 2.5m metric at WHU-TLC dataset,' but Table 1 shows the MAE improvement over SatMVS-F is only 1.895 to 1.879 (about 0.8%), and over SatMVS(RED-Net) it is 1.945 to 1.879 (about 3.4%). Moreover, on the <2.5m metric at 2048x1472, TS-SatMVSNet obtains 77.92% while SatMVS(RED-Net) obtains 77.93%, so the method is not strictly state-of-the-art on that metric. The abstract's unqualified 'state-of-the-art performance' claim should be revised to name specific metrics and baselines where the improvement is real.
  3. [Ablation Study, Table 3] The WHU-TLC* comparison in Table 3 does not support the claimed terrain-specific advantage because it changes the test set and the method simultaneously. The baseline rows are evaluated on WHU-TLC, while the final TS-SatMVSNet row is evaluated on WHU-TLC*, a subset constructed by the authors to contain only terrain areas. The reported improvements (MAE 2.026 to 1.793, <2.5m 77.01% to 79.03%) could reflect the easier, selected subset rather than the method. To support the claim, the same baselines must be evaluated on WHU-TLC* under identical conditions.
minor comments (4)
  1. [Methodology, Eq. (7)] Equation (7) uses the symbol ⊙ for what appears to be a convolution or sliding-window filtering operation, but the notation is not defined; also, the 'learnable Gaussian filter' has only a single learnable scalar i, so the description overstates the flexibility of this module.
  2. [Algorithm 1] Algorithm 1 is underspecified: the branch S_diff == 0 is not handled, and the final assignment 'Abs(S_diff + p3x3[indexmax])' is unclear without additional pseudocode or a precise tensor-indexing explanation.
  3. [Experimental Settings] The Experimental Settings section contains an incomplete cross-reference ('in Sec .') and the MVS3D preprocessing pipeline is described only as a promise to open-source later; enough detail should be provided for reproducibility of the reported MVS3D results.
  4. [Throughout] There are numerous typos and inconsistencies, including 'slop' for 'slope', 'Parition' for 'Partition', 'specifical' for 'specific', and duplicated table footnote lines in Table 2; a careful proofreading pass is needed.

Circularity Check

0 steps flagged · score 2.0 of 10

No material circularity; the only self-citation is minor and non-load-bearing, while the slope-direction loss issue is a correctness concern rather than a circular derivation.

full rationale

The paper's central height prediction is supervised directly by ground-truth height (Eq. 8) and is evaluated against external benchmarks, so no target result is fitted into the input. The slope map S(x)=|max(p3x3(x))-e| is a deterministic transform of the predicted height map, meaning the slope-guided modules repurpose the network's own intermediate representation; this is an architectural choice, not a circular derivation. The only self-citation is Zhang et al. 2023 (ARAI-MVSNet) for the all-pixel/pixel-wise height range (Figure 2 caption and Eqs. 2-3), which supplies a standard coarse-to-fine MVS heuristic and is not load-bearing for the slope contribution. The slope direction loss (Eq. 9) is intended as a self-supervised regularizer comparing predicted and pseudo-GT slope direction maps, both generated from height maps. As written, Algorithm 1 uses a hard argmax (`indexmax <- Max(p3x3)`), so the gradient of Ls with respect to the predicted height is zero almost everywhere; this is a serious implementation and correctness concern, not a circularity, and the absence of an ablation of Ls in Table 3 is a missing-support concern rather than evidence that the result reduces by construction. The paper also discloses its urban-area limitation, which is a scope caveat, not circularity. Overall, no prediction is equivalent to its input by construction, so the circularity score is low, with the minor self-citation accounting for the nonzero score.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The paper does not introduce new physical entities, but it relies on several assumptions: the 3x3 plane representation, the specific slope definition, prior RPC warping machinery, and a questionable loss design. The learnable Gaussian scalar is the only fitted parameter beyond standard hyperparameters. The core ideas are heuristics built on the authors' own earlier ARAI-MVSNet work.

free parameters (3)
  • Learnable scalar i in Gaussian filter kernel = not reported
    In Eq. 7, the entire 3x3 kernel is a fixed Gaussian template multiplied by a single learnable scalar i. This scalar is the only trainable parameter in the Height Correction Module and is learned from data.
  • Loss weights lambda1 and lambda2 = 0.5, 0.5
    Hyperparameters for combining height loss and slope direction loss in Eq. 10, set based on validation experiments.
  • Stage loss weights (0.5, 1.0, 2.0) = 0.5, 1.0, 2.0
    Weights for the three stages in both height and slope direction losses, set manually as is common in cascade MVS.
assumptions (4)
  • domain assumption The Earth's surface can be represented as countless 3x3 pixel planes with negligible internal height variation.
    Invoked in the Height based Slope Calculation Strategy and Height Correction Module; the paper assumes a 3x3 plane is the smallest meaningful slope surface.
  • ad hoc to paper Slope at a pixel can be approximated by the absolute height difference between the center pixel and the maximum height in its 3x3 neighborhood.
    Defined in Eq. 1 as S(x) = |max(p3x3) - center|, this is not a geometric slope but a local height variation metric, and the paper uses it as the basis for all slope-guided modules.
  • domain assumption The differentiable RPC warping module from SatMVS (Gao et al. 2021) is valid and applicable in this framework.
    The entire pipeline relies on this module for cost volume construction; it is taken as a given from prior work.
  • ad hoc to paper L2 loss on discrete slope direction codes 0-8 is a meaningful training signal.
    The slope direction loss in Eq. 9 applies MSE to nominal directional codes, which implies an ordinal relationship that does not exist; the paper provides no justification for this choice.

how reviews work

0 comments
Cite this review

Pith. "Pith review of TS-SatMVSNet: Slope Aware Height Estimation for Large-Scale Earth Terrain Multi-view Stereo." pith.science (2026). https://pith.science/paper/YMACNZKX

@misc{pith2026250101049,
  author       = {Pith},
  title        = {Pith review of: TS-SatMVSNet: Slope Aware Height Estimation for Large-Scale Earth Terrain Multi-view Stereo},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YMACNZKX}},
  note         = {Machine review of arXiv:2501.01049}
}
read the original abstract

3D terrain reconstruction with remote sensing imagery achieves cost-effective and large-scale earth observation and is crucial for safeguarding natural disasters, monitoring ecological changes, and preserving the environment.Recently, learning-based multi-view stereo~(MVS) methods have shown promise in this task. However, these methods simply modify the general learning-based MVS framework for height estimation, which overlooks the terrain characteristics and results in insufficient accuracy. Considering that the Earth's surface generally undulates with no drastic changes and can be measured by slope, integrating slope considerations into MVS frameworks could enhance the accuracy of terrain reconstructions. To this end, we propose an end-to-end slope-aware height estimation network named TS-SatMVSNet for large-scale remote sensing terrain reconstruction.To effectively obtain the slope representation, drawing from mathematical gradient concepts, we innovatively proposed a height-based slope calculation strategy to first calculate a slope map from a height map to measure the terrain undulation. To fully integrate slope information into the MVS pipeline, we separately design two slope-guided modules to enhance reconstruction outcomes at both micro and macro levels. Specifically, at the micro level, we designed a slope-guided interval partition module for refined height estimation using slope values. At the macro level, a height correction module is proposed, using a learnable Gaussian smoothing operator to amend the inaccurate height values. Additionally, to enhance the efficacy of height estimation, we proposed a slope direction loss for implicitly optimizing height estimation results. Extensive experiments on the WHU-TLC dataset and MVS3D dataset show that our proposed method achieves state-of-the-art performance and demonstrates competitive generalization ability.

Figures

Figures reproduced from arXiv: 2501.01049 by the authors.

Figure 1
Figure 1. Illustration of High-Resolution Remote Sensing [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Illustration of overall TS-SatMVSNet. This is a typical multi-stage coarse-to-fine framework. The modules labeled in [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 4
Figure 4. Illustration of Slope Directional Codes. [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figures from the paper (6 more)
Figure 5
Figure 5. Figure 5: Illustration of Height Correction Module. [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: Qualitative results of mainstream deep learning-based methods on two different areas of the WHU-TLC Dataset. The [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]
Figure 7
Figure 7. Figure 7: Visualization examples of the DSM results produced by the different methods on the WHU-TLC dataset. The red [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 8
Figure 8. Figure 8: Visualization examples of the slope maps, which calculated from the predicted height maps by our proposed Height [PITH_FULL_IMAGE:figures/full_fig_p010_8.png]
Figure 9
Figure 9. Figure 9: Visualization of the results of DSM evaluation in the first scene of the MVS3D dataset. Some qualitative results are [PITH_FULL_IMAGE:figures/full_fig_p012_9.png]
Figure 10
Figure 10. Figure 10: Visualization of some limited cases of mixed terrain and urban scenarios. [PITH_FULL_IMAGE:figures/full_fig_p013_10.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Dataset Construction for Training LLM to Learn Analog Circuit Knowledge

    cs.AR 2025-08 unverdicted novelty 5.0 of 10

    The abstract reports an analog-circuit LLM dataset and KL-regularized SFT gains, but the full text supplied is an unrelated 3D lane detection paper.

  2. SC-Lane: Slope-aware and Consistent Road Height Estimation Framework for 3D Lane Detection

    cs.CV 2025-08 unverdicted novelty 4.0 of 10

    SC-Lane improves road height estimation and 3D lane detection on OpenLane, reporting an F-score of 64.3%, via adaptive fusion of slope-specific features plus temporal consistency.

Reference graph

Works this paper leans on

47 extracted references · 42 canonical work pages · cited by 2 Pith papers

  1. [1]

    , author Lee, T.S

    author Abramson, L.W. , author Lee, T.S. , author Sharma, S. , author Boyce, G.M. , year 2001 . title Slope stability and stabilization methods . publisher John Wiley & Sons

    work page 2001

  2. [2]

    title Agisoft metashpe

    author Agisoft , year 2022 . title Agisoft metashpe . howpublished https://www.agisoft.com/

    work page 2022

  3. [3]

    title Ortho mapping with mosaic datasets—help | arcgis

    author ArcGIS , year 2022 . title Ortho mapping with mosaic datasets—help | arcgis . howpublished https://desktop.arcgis.com/en/arcmap/10.5/manage-data/rasterand-images/ortho-mapping-overview.htm

    work page 2022

  4. [4]

    , year 2006

    author Basher, R. , year 2006 . title Global early warning systems for natural hazards: systematic and people-centred . journal Philosophical transactions of the royal society a: mathematical, physical and engineering sciences volume 364 , pages 2167--2182

    2006

  5. [5]

    , author Foster, K

    author Bosch, M. , author Foster, K. , author Christie, G. , author Wang, S. , author Hager, G.D. , author Brown, M. , year 2019 . title Semantic stereo for incidental satellite images , in: booktitle 2019 IEEE Winter Conference on Applications of Computer Vision (WACV) , organization IEEE . pp. pages 1524--1532

    work page 2019

  6. [6]

    , author Kurtz, Z

    author Bosch, M. , author Kurtz, Z. , author Hagstrom, S. , author Brown, M. , year 2016 . title A multiple view stereo benchmark for satellite imagery , in: booktitle 2016 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) , organization IEEE . pp. pages 1--9

    work page 2016

  7. [7]

    title Catalyst professional – catalyst.earth [www document]

    author Catalyst , year 2021 . title Catalyst professional – catalyst.earth [www document] . howpublished https://catalyst.earth/products/catalyst-pro/

    work page 2021

  8. [8]

    , author He, J

    author Chang, J. , author He, J. , author Zhang, T. , author Yu, J. , author Wu, F. , year 2024 . title Ei-mvsnet: Epipolar-guided multi-view stereo network with interval-aware label . journal IEEE Transactions on Image Processing

    work page 2024

Show all 47 references
  1. [9]

    , author Yang, H.C

    author Chen, P.H. , author Yang, H.C. , author Chen, K.W. , author Chen, Y.S. , year 2020 . title Mvsnet++: Learning depth-based attention pyramid features for multi-view stereo . journal IEEE Transactions on Image Processing volume 29 , pages 7261--7273

    2020

  2. [10]

    , author Xu, Z

    author Cheng, S. , author Xu, Z. , author Zhu, S. , author Li, Z. , author Li, L.E. , author Ramamoorthi, R. , author Su, H. , year 2020 . title Deep stereo using adaptive thin volume representation with uncertainty awareness , in: booktitle Proceedings of the IEEE/CVF Confere...

    2020

  3. [11]

    , author Meinhardt-Llopis, E

    author De Franchis, C. , author Meinhardt-Llopis, E. , author Michel, J. , author Morel, J.M. , author Facciolo, G. , year 2014 a. title An automatic and modular stereo pipeline for pushbroom images . journal ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Infor...

    2014

  4. [12]

    , author Meinhardt-Llopis, E

    author De Franchis, C. , author Meinhardt-Llopis, E. , author Michel, J. , author Morel, J.M. , author Facciolo, G. , year 2014 b. title An automatic and modular stereo pipeline for pushbroom images . journal ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Infor...

    2014

  5. [13]

    , author Liu, J

    author Gao, J. , author Liu, J. , author Ji, S. , year 2021 . title Rational polynomial camera model warping for deep learning based satellite multi-view stereo matching , in: booktitle Proceedings of the IEEE/CVF International Conference on Computer Vision , pp. pages 6148--6157

    2021

  6. [14]

    , author Liu, J

    author Gao, J. , author Liu, J. , author Ji, S. , year 2023 . title A general deep learning based framework for 3d reconstruction from multi-view stereo satellite images . journal ISPRS Journal of Photogrammetry and Remote Sensing volume 195 , pages 446--461

    2023

  7. [15]

    , year 2015

    author Girshick, R. , year 2015 . title Fast r-cnn , in: booktitle Proceedings of the IEEE international conference on computer vision , pp. pages 1440--1448

    2015

  8. [16]

    , author Fan, Z

    author Gu, X. , author Fan, Z. , author Zhu, S. , author Dai, Z. , author Tan, F. , author Tan, P. , year 2020 . title Cascade cost volume for high-resolution multi-view stereo and stereo matching , in: booktitle Proceedings of the IEEE/CVF conference on computer vision and pa...

    2020

  9. [17]

    , year 2005

    author Hirschmuller, H. , year 2005 . title Accurate and efficient stereo processing by semi-global matching and mutual information , in: booktitle 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) , organization IEEE . pp. pages 807--814

    2005

  10. [18]

    , author Xiong, K

    author Ito, K. , author Xiong, K. , year 2000 . title Gaussian filters for nonlinear filtering problems . journal IEEE transactions on automatic control volume 45 , pages 910--927

    2000

  11. [19]

    , author Purves, R.S

    author Jones, C.B. , author Purves, R.S. , year 2008 . title Geographical information retrieval . journal International Journal of Geographical Information Science volume 22 , pages 219--228

    2008

  12. [20]

    , author Lu, Z

    author Li, J. , author Lu, Z. , author Wang, Y. , author Xiao, J. , author Wang, Y. , year 2023 . title Nr-mvsnet: Learning multi-view stereo based on normal consistency and depth refinement . journal IEEE Transactions on Image Processing

    2023

  13. [21]

    , author Wang, K

    author Li, Z. , author Wang, K. , author Zuo, W. , author Meng, D. , author Zhang, L. , year 2015 . title Detail-preserving and content-aware variational multi-view stereo reconstruction . journal IEEE Transactions on Image Processing volume 25 , pages 864--877

    2015

  14. [22]

    , author Doll \'a r, P

    author Lin, T.Y. , author Doll \'a r, P. , author Girshick, R. , author He, K. , author Hariharan, B. , author Belongie, S. , year 2017 . title Feature pyramid networks for object detection , in: booktitle Proceedings of the IEEE conference on computer vision and pattern recog...

    2017

  15. [23]

    , author Nearing, M.A

    author Liu, B.Y. , author Nearing, M.A. , author Risse, L. , year 1994 . title Slope gradient effects on soil loss for steep slopes . journal Transactions of the ASAE volume 37 , pages 1835--1840

    1994

  16. [24]

    , author Ji, S

    author Liu, J. , author Ji, S. , year 2020 . title A novel recurrent encoder-decoder structure for large-scale multi-view stereo reconstruction from an open aerial dataset , in: booktitle Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pp. p...

    2020

  17. [25]

    , author Wang, P

    author Liu, N. , author Wang, P. , author Xiang, S. , author Gu, N. , author Wang, F. , year 2023 . title Rs-mvsnet: Inferring the earth's digital surface model from multi-view optical remote sensing images , in: booktitle IECON 2023-49th Annual Conference of the IEEE Industri...

    2023

  18. [26]

    , author Guan, T

    author Luo, K. , author Guan, T. , author Ju, L. , author Huang, H. , author Luo, Y. , year 2019 . title P-mvsnet: Learning patch-wise matching confidence aggregation for multi-view stereo , in: booktitle Proceedings of the IEEE/CVF International Conference on Computer Vision ...

    2019

  19. [27]

    , author Liu, Y

    author Meng, H. , author Liu, Y. , author Zhang, J. , author Gong, H. , year 2007 . title Positional accuracy in rpc point determination based on high-resolution imagery , in: booktitle Geoinformatics 2007: Remotely Sensed Data and Information , organization SPIE . pp. pages 1...

    2007

  20. [28]

    , author Henikoff, S

    author Ng, P.C. , author Henikoff, S. , year 2003 . title Sift: Predicting amino acid changes that affect protein function . journal Nucleic acids research volume 31 , pages 3812--3814

    2003

  21. [29]

    , author Gross, S

    author Paszke, A. , author Gross, S. , author Massa, F. , author Lerer, A. , author Bradbury, J. , author Chanan, G. , author Killeen, T. , author Lin, Z. , author Gimelshein, N. , author Antiga, L. , et al., year 2019 . title Pytorch: An imperative style, high-performance dee...

    2019

  22. [30]

    , author Chabert, M

    author Prendes, J. , author Chabert, M. , author Pascal, F. , author Giros, A. , author Tourneret, J.Y. , year 2014 . title A new multivariate statistical model for change detection in images acquired by homogeneous and heterogeneous sensors . journal IEEE Transactions on Imag...

    2014

  23. [31]

    , year 2016

    author Qin, R. , year 2016 . title Rpc stereo processor (rsp)--a software package for digital surface model and orthophoto generation from satellite stereo imagery . journal ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences volume 3 , pages 77--82

    2016

  24. [32]

    , author Frahm, J.M

    author Schonberger, J.L. , author Frahm, J.M. , year 2016 . title Structure-from-motion revisited , in: booktitle Proceedings of the IEEE conference on computer vision and pattern recognition , pp. pages 4104--4113

    2016

  25. [33]

    title iarpa: My iarpa contest submission

    author SDRDIS , year 2016 . title iarpa: My iarpa contest submission . howpublished https://github.com/sdrdis/iarpa

    2016

  26. [34]

    , author Curless, B

    author Seitz, S.M. , author Curless, B. , author Diebel, J. , author Scharstein, D. , author Szeliski, R. , year 2006 . title A comparison and evaluation of multi-view stereo reconstruction algorithms , in: booktitle 2006 IEEE computer society conference on computer vision and...

    2006

  27. [35]

    , year 2005

    author Spellerberg, I.F. , year 2005 . title Monitoring ecological change . publisher Cambridge University Press

    2005

  28. [36]

    , year 2010

    author Stereopsis, R.M. , year 2010 . title Accurate, dense, and robust multiview stereopsis . journal IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE volume 32

    2010

  29. [37]

    , author Chen, K

    author Sun, L. , author Chen, K. , author Song, M. , author Tao, D. , author Chen, G. , author Chen, C. , year 2017 . title Robust, efficient depth reconstruction with hierarchical confidence-based matching . journal IEEE Transactions on Image Processing volume 26 , pages 3331--3343

    2017

  30. [38]

    , year 1978

    author Varnes, D.J. , year 1978 . title Slope movement types and processes . journal Special report volume 176 , pages 11--33

    1978

  31. [39]

    , author Juarez-Valdes, E.L

    author Whitaker, R.T. , author Juarez-Valdes, E.L. , year 2002 . title On the reconstruction of height functions and terrain maps from dense range data . journal IEEE transactions on image processing volume 11 , pages 704--716

    2002

  32. [40]

    , author Zhang, Y

    author Xiong, Z. , author Zhang, Y. , year 2010 . title Bundle adjustment with rational polynomial camera models based on generic method . journal IEEE Transactions on Geoscience and Remote Sensing volume 49 , pages 190--202

    2010

  33. [41]

    , author Luo, Z

    author Yao, Y. , author Luo, Z. , author Li, S. , author Fang, T. , author Quan, L. , year 2018 . title Mvsnet: Depth inference for unstructured multi-view stereo , in: booktitle Proceedings of the European conference on computer vision (ECCV) , pp. pages 767--783

    2018

  34. [42]

    , author Luo, Z

    author Yao, Y. , author Luo, Z. , author Li, S. , author Shen, T. , author Fang, T. , author Quan, L. , year 2019 . title Recurrent mvsnet for high-resolution multi-view stereo depth inference , in: booktitle Proceedings of the IEEE/CVF conference on computer vision and patter...

    2019

  35. [43]

    , author Gao, S

    author Yu, Z. , author Gao, S. , year 2020 . title Fast-mvsnet: Sparse-to-dense multi-view stereo with learned propagation and gauss-newton refinement , in: booktitle Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pp. pages 1949--1958

    2020

  36. [44]

    , author Snavely, N

    author Zhang, K. , author Snavely, N. , author Sun, J. , year 2019 . title Leveraging vision reconstruction pipelines for satellite imagery , in: booktitle Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops , pp. pages 0--0

    2019

  37. [45]

    , author Xu, W

    author Zhang, S. , author Xu, W. , author Wei, Z. , author Zhang, L. , author Wang, Y. , author Liu, J. , year 2023 . title Arai-mvsnet: A multi-view stereo depth estimation network with adaptive depth range and depth interval . journal Pattern Recognition volume 144 , pages 109885

    2023

  38. [46]

    , " * 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...

  39. [47]

    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 gl...

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.