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arxiv: 2511.00392 · v2 · pith:5MDWWSYDnew · submitted 2025-11-01 · 💻 cs.RO · cs.AI· cs.CV

SonarSweep: Fusing Sonar and Vision for Robust 3D Reconstruction via Plane Sweeping

Lingpeng Chen , Jiakun Tang , Apple Pui-Yi Chui , Ziyang Hong , Junfeng Wu This is my paper

Pith reviewed 2026-05-21 19:29 UTC · model grok-4.3

classification 💻 cs.RO cs.AIcs.CV
keywords sonar vision fusionunderwater 3D reconstructionplane sweepingdepth estimationmulti-modal sensingdeep learningturbid environmentsrobotics
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The pith

Adapting the plane sweep algorithm for sonar-vision fusion in deep learning produces dense accurate underwater depth maps.

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

The paper tries to establish that an end-to-end deep learning framework can adapt the principled plane sweep algorithm to fuse sonar and visual data for 3D reconstruction. A sympathetic reader would care because vision fails in poor visibility and sonar alone has elevation ambiguity plus low resolution, while earlier fusion relied on heuristics that created artifacts in complex scenes. The approach generates dense depth maps that outperform prior methods especially in high turbidity. Experiments cover both simulation and real environments, and the authors release code plus a new synchronized stereo-camera and sonar dataset.

Core claim

SonarSweep adapts the principled plane sweep algorithm within an end-to-end deep learning framework to perform cross-modal fusion between sonar and visual data, enabling the generation of dense and accurate depth maps that outperform state-of-the-art methods in underwater environments, particularly under high turbidity.

What carries the argument

The adapted plane sweep algorithm for cross-modal fusion, which sweeps through depth planes and matches features from both sonar and vision to resolve ambiguities without extra heuristics.

If this is right

  • Dense depth maps become available in high turbidity where vision-based methods fail.
  • Sonar's elevation ambiguity is resolved through integration with visual data.
  • Complex scenes can be modeled without the artifacts produced by prior heuristic fusion techniques.
  • Robust performance holds across both high-fidelity simulation and real-world tests.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same geometric fusion idea could be tested for other sensor pairs in low-visibility settings such as dust or fog.
  • Real-time versions might improve autonomous underwater vehicle navigation and obstacle avoidance.
  • The released synchronized dataset allows other groups to train and compare alternative fusion networks directly.

Load-bearing premise

The principled plane sweep algorithm can be directly adapted for effective cross-modal fusion between sonar and visual data without introducing new geometric artifacts or requiring additional heuristics in complex scenes.

What would settle it

Running the released real-world dataset in high-turbidity conditions and finding that SonarSweep depth maps contain more artifacts or lower accuracy than heuristic-based fusion methods would disprove the central claim.

Figures

Figures reproduced from arXiv: 2511.00392 by Apple Pui-Yi Chui, Jiakun Tang, Junfeng Wu, Lingpeng Chen, Ziyang Hong.

Figure 1
Figure 1. Figure 1: The SonarSweep System. (Left) The experimental AUV in a challenging underwater environment. (Top Right) The integrated camera and sonar sensor suite. (Bottom Right) Conceptual diagram of the fusion approach. fusion techniques have failed to provide a complete solution. Vision-led SLAM systems that use sonar for scale correc￾tion fail when visual features are lost in turbid water [8], [9]. Computationally e… view at source ↗
Figure 2
Figure 2. Figure 2: The Forward-Looking Sonar (FLS) sensor model. A [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: An overview of the SonarSweep pipeline. From a synchronized sonar and camera image pair, we extract feature maps [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: (a) Geometric parameterization of a candidate plane, [PITH_FULL_IMAGE:figures/full_fig_p003_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: From left to right: the simulated underwater world in OceanSim with varied water conditions; the physical lab pool [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The sonar image preprocessing pipeline. 4) Sim-to-Real Training Strategy: To bridge the substan￾tial domain gap between simulated and real-world data (see [PITH_FULL_IMAGE:figures/full_fig_p006_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Qualitative comparison of each methods in simulated (Sim case 1 & 2) and real-world (Real case 1 & 2) scenarios. [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Quantitative comparison of absolute error versus [PITH_FULL_IMAGE:figures/full_fig_p007_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Synthesized images for mild, moderate, and high turbidity, followed by performance analysis [PITH_FULL_IMAGE:figures/full_fig_p008_9.png] view at source ↗
read the original abstract

Accurate 3D reconstruction in visually-degraded underwater environments remains a formidable challenge. Single-modality approaches are insufficient: vision-based methods fail due to poor visibility and geometric constraints, while sonar is crippled by inherent elevation ambiguity and low resolution. Consequently, prior fusion technique relies on heuristics and flawed geometric assumptions, leading to significant artifacts and an inability to model complex scenes. In this paper, we introduce SonarSweep, a novel, end-to-end deep learning framework that overcomes these limitations by adapting the principled plane sweep algorithm for cross-modal fusion between sonar and visual data. Extensive experiments in both high-fidelity simulation and real-world environments demonstrate that SonarSweep consistently generates dense and accurate depth maps, significantly outperforming state-of-the-art methods across challenging conditions, particularly in high turbidity. To foster further research, we will publicly release our code and a novel dataset featuring synchronized stereo-camera and sonar data, the first of its kind.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces SonarSweep, an end-to-end deep learning framework that adapts the classical plane-sweep algorithm for cross-modal fusion of sonar range-bearing measurements and visual imagery. It claims this yields dense, accurate depth maps in underwater settings where single-modality approaches fail, with consistent outperformance over prior fusion methods especially under high turbidity; experiments are reported in high-fidelity simulation and real-world environments, and the authors commit to releasing code plus a new synchronized stereo-sonar dataset.

Significance. A rigorously derived plane-sweep fusion that correctly maps conical sonar returns onto swept planes while preserving geometric consistency would address a long-standing gap in underwater perception and could enable more reliable dense reconstruction for AUV navigation and mapping. The promised public release of code and the first synchronized stereo-sonar dataset would add substantial community value if the core geometric adaptation is shown to be artifact-free.

major comments (2)
  1. [§3.2] §3.2 (Cost-volume construction): the manuscript must supply an explicit geometric derivation showing how a sonar range-bearing observation intersects a given fronto-parallel plane, how elevation ambiguity is resolved or marginalized, and how the resulting cost is aggregated with visual features. Without this derivation the adaptation cannot be called 'principled' and risks re-introducing the heuristic assumptions the abstract criticizes in prior work.
  2. [§4] §4 (Experiments): quantitative results are presented without error bars, ablation studies isolating the sonar-to-plane projection, or clear dataset statistics (number of frames, turbidity levels, ground-truth acquisition method). These omissions prevent verification that the reported outperformance is attributable to the claimed geometric fusion rather than implementation details or dataset bias.
minor comments (2)
  1. [Figure 3] Figure 3 caption and §3.1: the notation for the sonar projection matrix is introduced without a preceding equation; add an explicit definition before its first use.
  2. [Related Work] Related-work section: several recent sonar-vision fusion papers (e.g., 2023–2024) are cited only by name; add one-sentence summaries of their geometric assumptions to clarify the precise novelty of SonarSweep.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed review. The suggestions to strengthen the geometric derivation and experimental reporting are valuable, and we have revised the manuscript to address them directly while preserving the core contributions of SonarSweep.

read point-by-point responses

  1. Referee: [§3.2] §3.2 (Cost-volume construction): the manuscript must supply an explicit geometric derivation showing how a sonar range-bearing observation intersects a given fronto-parallel plane, how elevation ambiguity is resolved or marginalized, and how the resulting cost is aggregated with visual features. Without this derivation the adaptation cannot be called 'principled' and risks re-introducing the heuristic assumptions the abstract criticizes in prior work.

    Authors: We appreciate the referee's call for an explicit geometric derivation. The original §3.2 describes the sonar-to-plane mapping and cost-volume construction at a high level, but we agree that a self-contained derivation is needed to fully substantiate the claim of a principled adaptation. In the revised manuscript we have inserted a new subsection that derives: (1) the intersection geometry of a conical sonar range-bearing measurement with a fronto-parallel plane at hypothesized depth d, (2) marginalization of elevation ambiguity via integration over the elevation angle consistent with the plane intersection (or learned probabilistic weighting within the network), and (3) the subsequent aggregation of the resulting sonar cost with visual feature similarity scores. This derivation is free of the heuristic assumptions criticized in prior fusion work and directly supports the end-to-end training. revision: yes

  2. Referee: [§4] §4 (Experiments): quantitative results are presented without error bars, ablation studies isolating the sonar-to-plane projection, or clear dataset statistics (number of frames, turbidity levels, ground-truth acquisition method). These omissions prevent verification that the reported outperformance is attributable to the claimed geometric fusion rather than implementation details or dataset bias.

    Authors: We agree that the experimental section would benefit from these additions to allow independent verification. In the revised §4 we now report error bars (standard deviation across repeated training runs and cross-validation folds) on all quantitative metrics, include dedicated ablation studies that isolate the sonar-to-plane projection component by comparing full SonarSweep against variants that replace the geometric mapping with simpler heuristics, and provide expanded dataset statistics: exact frame counts, simulated and measured turbidity levels (with corresponding attenuation coefficients), and ground-truth acquisition details (simulation ray-tracing for synthetic data; synchronized stereo reconstruction in a controlled tank for real data). These revisions confirm that the observed gains are attributable to the geometric fusion. revision: yes

Circularity Check

0 steps flagged

No significant circularity in claimed adaptation of plane sweep

full rationale

The paper presents SonarSweep as a novel end-to-end framework adapting the established plane-sweep algorithm for cross-modal sonar-vision fusion. The abstract and provided description introduce the method without any equations, fitted parameters, or self-citations that reduce the output depth maps or fusion process to the inputs by construction. The central claim of principled adaptation is supported by claimed experimental validation in simulation and real environments rather than by definitional equivalence or load-bearing self-reference. This is the most common honest finding for papers that build on standard algorithms like plane sweeping without internal reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no explicit free parameters, axioms, or invented entities are identifiable. The approach inherits standard deep-learning training assumptions and the geometric plane-sweep principle from prior literature.

pith-pipeline@v0.9.0 · 5707 in / 1130 out tokens · 101137 ms · 2026-05-21T19:29:47.993453+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

23 extracted references · 23 canonical work pages · 2 internal anchors

  1. [1]

    Mapping 3d underwater environments with smoothed submaps,

    M. VanMiddlesworth, M. Kaess, F. Hover, and J. J. Leonard, “Mapping 3d underwater environments with smoothed submaps,” inField and Service Robotics: Results of the 9th International Conference, Springer, 2015, pp. 17–30

    work page 2015

  2. [2]

    Inspection and maintenance of industrial infrastructure with autonomous underwater robots,

    F. Nauert and P. Kampmann, “Inspection and maintenance of industrial infrastructure with autonomous underwater robots,”Frontiers in Robotics and AI, vol. 10, p. 1 240 276, 2023

    work page 2023

  3. [3]

    Limi- tations of vision guided underwater navigation,

    M. E. Angelopoulou, C. Tsiotsios, and M. Petrou, “Limi- tations of vision guided underwater navigation,”IFAC Pro- ceedings Volumes, vol. 45, no. 5, pp. 312–317, 2012

    work page 2012

  4. [4]

    Turbid-water subsea infrastructure 3d recon- struction with assisted stereo,

    R. Detry et al., “Turbid-water subsea infrastructure 3d recon- struction with assisted stereo,” in2018 OCEANS-MTS/IEEE Kobe Techno-Oceans (OTO), IEEE, 2018, pp. 1–6

    work page 2018

  5. [5]

    Y . Wang, Y . Ji, H. Tsuchiya, H. Asama, and A. Ya- mashita,Learning pseudo front depth for 2d forward-looking sonar-based multi-view stereo, 2022. arXiv:2208.00233 [cs.CV]. [Online]. Available:https://arxiv.org/ abs/2208.00233

    work page arXiv 2022

  6. [6]

    3d recon- struction using multiple acoustic images under roll motion based on backprojection techniques,

    J.-Y . Park, H. Baek, B.-H. Jun, and P.-M. Lee, “3d recon- struction using multiple acoustic images under roll motion based on backprojection techniques,” inOCEANS 2023 - MTS/IEEE U.S. Gulf Coast, 2023, pp. 1–4.DOI:10 . 23919/OCEANS52994.2023.10337360

    work page arXiv 2023

  7. [7]

    Fusing concur- rent orthogonal wide-aperture sonar images for dense under- water 3d reconstruction,

    J. McConnell, J. D. Martin, and B. Englot, “Fusing concur- rent orthogonal wide-aperture sonar images for dense under- water 3d reconstruction,” in2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020, pp. 1653–1660.DOI:10 . 1109 / IROS45743 . 2020 . 9340995

    work page 2020

  8. [8]

    Map building fusing acoustic and visual information using autonomous underwater vehicles,

    C. Kunz and H. Singh, “Map building fusing acoustic and visual information using autonomous underwater vehicles,” Journal of field robotics, vol. 30, no. 5, pp. 763–783, 2013

    work page 2013

  9. [9]

    Underwater monocular image depth estimation using single-beam echosounder,

    M. Roznere and A. Q. Li, “Underwater monocular image depth estimation using single-beam echosounder,” in2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 2020, pp. 1785–1790

    work page 2020

  10. [10]

    Qadri, K

    M. Qadri, K. Zhang, A. Hinduja, M. Kaess, A. Pediredla, and C. A. Metzler,Aoneus: A neural rendering framework for acoustic-optical sensor fusion, 2024. arXiv:2402.03309 [cs.CV]. [Online]. Available:https://arxiv.org/ abs/2402.03309

    work page arXiv 2024

  11. [11]

    Collado-Gonzalez, J

    I. Collado-Gonzalez, J. McConnell, P. Szenher, and B. Englot,Opti-acoustic scene reconstruction in highly tur- bid underwater environments, 2025. arXiv:2508.03408 [cs.RO]. [Online]. Available:https://arxiv.org/ abs/2508.03408

    work page arXiv 2025

  12. [12]

    DPSNet: End-to-end Deep Plane Sweep Stereo

    S. Im, H.-G. Jeon, S. Lin, and I. S. Kweon,Dpsnet: End-to- end deep plane sweep stereo, 2019. arXiv:1905.00538 [cs.CV]. [Online]. Available:https://arxiv.org/ abs/1905.00538

    work page internal anchor Pith review Pith/arXiv arXiv 2019

  13. [13]

    Y . Yao, Z. Luo, S. Li, T. Fang, and L. Quan,Mvsnet: Depth inference for unstructured multi-view stereo, 2018. arXiv: 1804.02505 [cs.CV]. [Online]. Available:https:// arxiv.org/abs/1804.02505

    work page internal anchor Pith review Pith/arXiv arXiv 2018

  14. [14]

    Fourier-based registration for robust forward-looking sonar mosaicing in low-visibility underwater environments,

    N. Hurtos, D. Ribas, X. Cufi, Y . Petillot, and J. Salvi, “Fourier-based registration for robust forward-looking sonar mosaicing in low-visibility underwater environments,”Jour- nal of Field Robotics, vol. 32, no. 1, pp. 123–151, 2015

    work page 2015

  15. [15]

    B. Wen, M. Trepte, J. Aribido, J. Kautz, O. Gallo, and S. Birchfield,Foundationstereo: Zero-shot stereo matching,

    work page

  16. [16]

    Foundationstereo: Zero-shot stereo matching

    arXiv:2501.09898 [cs.CV]. [Online]. Available: https://arxiv.org/abs/2501.09898

    work page arXiv

  17. [17]

    Agisoft LLC,Agisoft metashape professional,https:// www.agisoft.com/, Version 2.0.2, 2023

    work page 2023

  18. [18]

    Flsea: Underwater visual-inertial and stereo- vision forward-looking datasets,

    Y . Randall, “Flsea: Underwater visual-inertial and stereo- vision forward-looking datasets,” M.S. thesis, University of Haifa (Israel), 2023

    work page 2023

  19. [19]

    Oceansim: A gpu-accelerated underwa- ter robot perception simulation framework,

    J. Song, H. Ma, O. Bagoren, A. V . Sethuraman, Y . Zhang, and K. A. Skinner, “Oceansim: A gpu-accelerated underwa- ter robot perception simulation framework,”arXiv preprint arXiv:2503.01074, 2025

    work page arXiv 2025

  20. [20]

    Differentiable space carving for 3d reconstruction using imaging sonar,

    Y . Feng, W. Lu, H. Gao, B. Nie, K. Lin, and L. Hu, “Differentiable space carving for 3d reconstruction using imaging sonar,”IEEE Robotics and Automation Letters, vol. 9, no. 11, pp. 10 065–10 072, 2024.DOI:10.1109/ LRA.2024.3469778

    work page arXiv 2024

  21. [21]

    Practical blind image denoising via swin- conv-unet and data synthesis,

    K. Zhang et al., “Practical blind image denoising via swin- conv-unet and data synthesis,”Machine Intelligence Re- search, vol. 20, no. 6, pp. 822–836, Sep. 2023,ISSN: 2731- 5398.DOI:10.1007/s11633- 023- 1466- 0[Online]. Available:http://dx.doi.org/10.1007/s11633- 023-1466-0

    work page doi:10.1007/s11633- 2023

  22. [22]

    An image synthesis method generating underwater images,

    J. R. Ahamed, P. E. Abas, and L. C. De Silva, “An image synthesis method generating underwater images,”Advances in Technology Innovation, vol. 7, no. 3, p. 195, 2022

    work page 2022

  23. [23]

    N. G. Jerlov and F. F. Koczy,Photographic measurements of daylight in deep water. Elanders boktr., 1951

    work page 1951