ACSim: A Novel Acoustic Camera Simulator With Recursive Ray Tracing, Artifact Modeling, and Ground Truthing
Yusheng Wang, Yonghoon Ji, Hiroshi Tsuchiya, Jun Ota, Hajime Asama, Atsushi Yamashita · IEEE Transactions on Robotics · 2025
We present a novel acoustic camera simulator (ACSim) that generates realistic sonar images by incorporating recursive ray tracing and sonar artifact modeling and provides various ground truth labels, enabling benchmarking and learning purposes. The 2D forward-looking sonar (FLS), also known as the acoustic camera, produces high-quality 2D images. Conducting real-world underwater experiments is challenging, making realistic sonar image simulation a necessary alternative. However, existing simulators often lack sufficient realism or are limited to specific scenes and phenomena. As a result, training on simulations and testing on real sonar images (i.e., sim-to-real) remains an open problem for deep learning-based applications. Our work introduces a novel sonar simulator with a customized rendering engine. We use recursive ray tracing to model multipath reflections in arbitrary scenes and propose physics-based shading for intensity computation. We propose a resampling method for anti-aliasing and model significant artifacts such as rolling shutter distortions and cross-talk noise. The simulator provides various ground truths for benchmarking and deep learning applications. We tested several tasks by training on synthetic images and demonstrated that the models also work on real images. We developed a Blender add-on for an enhanced user interface and will make the simulator open-source to advance future research.