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arxiv: 2605.19712 · v1 · pith:I2ZANHYQnew · submitted 2026-05-19 · 💻 cs.CV

Physics-informed simulation framework for realistic sonar image generation and statistical validation

Kamal Basha S , Athira Nambiar This is my paper

Pith reviewed 2026-05-20 05:57 UTC · model grok-4.3

classification 💻 cs.CV
keywords sonar simulationGazebophysics-informedstatistical validationKL divergencesynthetic dataunderwater imaginglocal binary patterns
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The pith

A Gazebo-based physics simulation generates sonar images that match real data texture distributions with KL divergence below 0.07.

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

The paper introduces a simulation platform that builds synthetic sonar images inside a Gazebo virtual world by directly setting seabed surface details, shadow patterns from light and object geometry, sensor height above the bottom, and added noise. It then checks how well these images line up with two public real sonar collections by comparing their brightness histograms and local texture patterns through statistical distance measures. The results indicate close texture agreement across object types and somewhat better intensity matches for simpler shapes. This approach addresses the high cost and scarcity of real underwater sonar recordings by offering a controlled way to produce and verify large volumes of usable synthetic data for vision tasks.

Core claim

ACOUSIM generates sonar-like images in Gazebo by explicitly setting seabed texture, illumination-driven shadowing, platform altitude, and noise levels, then quantifies their realism against real datasets using global intensity histograms and local binary pattern textures evaluated by Kullback-Leibler, Jensen-Shannon, and Earth Mover's distances, achieving texture KL values below 0.07 for all classes while noting stronger intensity alignment for plane targets than for ships owing to differences in shadow complexity.

What carries the argument

The ACOUSIM platform that parameterizes physical sonar imaging elements inside Gazebo and measures statistical match via intensity and LBP distribution distances.

Load-bearing premise

That setting seabed texture, shadowing, altitude, and noise inside the Gazebo simulator produces images whose intensity and texture statistics are close enough to real sonar returns for the chosen divergence metrics to serve as valid indicators of realism.

What would settle it

Collecting a fresh set of real sonar images from a different seabed or sensor setup and finding that the LBP texture KL divergence rises well above 0.07 would show the simulation parameters do not produce sufficiently representative data.

read the original abstract

Synthetic sonar datasets offer a scalable alternative to costly real-world acquisition, yet their utility remains limited by the absence of rigorous quantitative validation. We present ACOUSIM (ACOustic SIMulation and Validation Platform), a physics-informed framework that evaluates the statistical alignment between synthetic and real sonar imagery without relying on generative models. A Gazebo-based environment generates sonar-like images by explicitly controlling seabed texture, illumination-driven shadowing, platform altitude, and noise. Realism is quantified against two public sonar datasets, SeabedObjects-KLSG-II and Sonar Common Target Detection (SCTD), using global intensity and local texture (LBP) distributions assessed via Kullback-Leibler divergence, Jensen-Shannon divergence, and Earth Mover's Distance. Results show strong texture alignment (KL < 0.07) across all classes, with plane-class intensity alignment outperforming ship-class due to shadow geometry complexity. ACOUSIM establishes a reproducible, distribution-level baseline for sim-to-real sonar evaluation and directly supports reliable dataset validation for underwater image analysis.

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 manuscript introduces ACOUSIM, a Gazebo-based physics-informed framework for generating synthetic sonar images by explicitly controlling seabed texture, illumination-driven shadowing, platform altitude, and noise. Realism is assessed against the public SeabedObjects-KLSG-II and SCTD datasets via comparisons of global intensity and local LBP texture distributions using KL divergence, JS divergence, and EMD, with reported strong texture alignment (KL < 0.07) and better intensity alignment for plane-class images than ship-class due to shadow complexity. The work positions itself as establishing a reproducible, distribution-level baseline for sim-to-real sonar evaluation without generative models.

Significance. If the results and modeling hold, the paper supplies a useful, reproducible baseline for quantitative sim-to-real validation in sonar imagery using public datasets and standard distributional metrics. Explicit parameter control and the focus on statistical rather than perceptual or generative alignment are strengths that could support downstream underwater computer vision dataset curation.

major comments (2)
  1. [Abstract] Abstract: The central claim that the framework is 'physics-informed' for realistic sonar image generation rests on Gazebo simulation with 'illumination-driven shadowing.' This phrasing indicates geometric occlusion under optical lighting rather than solution of the acoustic wave equation, frequency-dependent attenuation, reverberation, or multipath. Without explicit mapping or justification of how the controlled parameters reproduce sonar physics (as opposed to visual rendering), the reported KL/JS/EMD alignments on intensity and LBP may reflect texture/noise tuning rather than physical fidelity, directly affecting the validity of the sim-to-real baseline claim.
  2. [Results] Results (as summarized in abstract): The post-hoc attribution of superior plane-class intensity alignment to 'shadow geometry complexity' is presented without accompanying per-class metric tables, distribution plots, or ablation on shadow parameters. This leaves the cross-class comparison vulnerable to unstated implementation choices in the Gazebo setup and weakens support for the overall statistical validation narrative.
minor comments (2)
  1. The abstract states results and metrics but provides no derivation details or error analysis; the full manuscript should include these to allow independent assessment of the reported divergences.
  2. Acronyms (LBP, KL, JS, EMD) and the exact formulas or implementations used for the distributional metrics should be defined with equations on first use for clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below with clarifications and indicate where the manuscript has been revised.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that the framework is 'physics-informed' for realistic sonar image generation rests on Gazebo simulation with 'illumination-driven shadowing.' This phrasing indicates geometric occlusion under optical lighting rather than solution of the acoustic wave equation, frequency-dependent attenuation, reverberation, or multipath. Without explicit mapping or justification of how the controlled parameters reproduce sonar physics (as opposed to visual rendering), the reported KL/JS/EMD alignments on intensity and LBP may reflect texture/noise tuning rather than physical fidelity, directly affecting the validity of the sim-to-real baseline claim.

    Authors: We agree that the 'physics-informed' designation benefits from explicit justification. ACOUSIM prioritizes geometric and parametric modeling of effects central to sonar image formation, such as occlusion-based shadowing, altitude-dependent incidence, and texture-driven scattering returns. These are established physical contributors in sonar literature, even if the implementation uses Gazebo's rendering engine rather than a full wave-propagation solver. In the revision we have added a dedicated subsection that maps each controlled parameter (seabed texture, altitude, shadowing geometry, noise) to corresponding sonar physics principles with supporting citations, thereby clarifying that the reported distributional alignments arise from physically motivated settings rather than post-hoc tuning. revision: yes

  2. Referee: [Results] Results (as summarized in abstract): The post-hoc attribution of superior plane-class intensity alignment to 'shadow geometry complexity' is presented without accompanying per-class metric tables, distribution plots, or ablation on shadow parameters. This leaves the cross-class comparison vulnerable to unstated implementation choices in the Gazebo setup and weakens support for the overall statistical validation narrative.

    Authors: We accept that additional supporting material is required to substantiate the class-wise observations. The revised manuscript now includes (i) per-class tables of KL, JS, and EMD values for both global intensity and local LBP distributions, (ii) corresponding histogram and cumulative distribution plots, and (iii) an ablation study that systematically varies shadow-related parameters (object height and platform altitude) while holding other factors fixed. These additions directly demonstrate the contribution of shadow geometry to the observed intensity-alignment differences between plane and ship classes. revision: yes

Circularity Check

0 steps flagged

No significant circularity; validation grounded in external datasets

full rationale

The paper presents a Gazebo simulation that generates images via explicit parameter control (seabed texture, shadowing, altitude, noise) and then performs statistical comparison to independent public datasets (SeabedObjects-KLSG-II and SCTD) using KL, JS, and EMD metrics on intensity and LBP features. No equations or steps reduce predictions to fitted inputs by construction, and no self-citations are invoked as load-bearing uniqueness theorems or ansatzes. The central claim of statistical alignment rests on external real-world data rather than internal redefinitions or renamings, rendering the derivation self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that Gazebo controls capture enough sonar physics for distribution-level realism; no free parameters, invented entities, or additional axioms are stated in the abstract.

axioms (1)
  • domain assumption Gazebo simulation with explicit controls over seabed texture, illumination-driven shadowing, platform altitude, and noise sufficiently models real sonar image formation for statistical validation.
    Invoked to justify generation of sonar-like images and their comparison to real data.

pith-pipeline@v0.9.0 · 5704 in / 1396 out tokens · 63482 ms · 2026-05-20T05:57:29.676180+00:00 · methodology

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Reference graph

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