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

TL;DR

This paper introduces ACOUSIM, a physics-informed simulation platform that rigorously evaluates the statistical similarity between synthetic and real sonar images, enhancing validation methods for underwater imaging datasets.

Contribution

The paper presents a novel physics-based framework for quantitative validation of synthetic sonar data, moving beyond generative models to statistical alignment assessment.

Findings

Strong texture alignment with KL < 0.07 across classes

Plane-class intensity matches better than ship-class due to shadow complexity

Establishes a reproducible baseline for sim-to-real sonar evaluation

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.