Aqua-Splat: Physically-Informed Sonar-Camera Gaussian Splatting for Underwater 3D Reconstruction

Differentiable Gaussian Splatting (GS) has emerged as a powerful paradigm for scene representation, enabling efficient rendering and real-time editing. However, existing GS-based methods, which rely mainly on clear visual images, perform poorly in underwater environments due to camera distortions such as light absorption and backscattering. In contrast, acoustic sensors like Forward Looking Sonar (FLS) offer superior penetration and robustness in such conditions. To leverage the complementary merits of visual and FLS images, we propose a novel GS framework customized for underwater scenarios, termed Aqua-Splat, for robust and accurate underwater perception. It ensures physically consistent reconstruction by incorporating the sonar wave propagation modeling in the image formation process. Moreover, we propose a volume rendering technique for sonar image synthesis, achieving similar speed to visual rendering. Additionally, we introduce a sonar-guided densification strategy to optimize the scene representation. Through extensive experiments on both simulated and datasets from the lab pool, we demonstrate that Aqua-Splat significantly improves image synthesis and 3D scene reconstruction in challenging underwater environments, outperforming existing methods in terms of both geometric accuracy and photometric fidelity. The code of Aqua-Splat will be open-sourced later for the community.