Shallow-water environments present significant challenges for underwater photogrammetry due to light caustics and the combined effects of absorption and scattering caused by water turbidity. These optical disturbances degrade image quality, disrupt feature matching, and ultimately reduce the reliability of 3D reconstruction using traditional SfM (Structure from Motion) pipeline. In this study, we focus on these two dominant factors and investigate a 3D reconstruction framework inspired by recent feed-forward architectures such as DUSt3R (Dense and Unconstrained Stereo 3D Reconstruction). To support this approach, we develop a synthetic data generation pipeline capable of simulating shallow-water visual conditions. Preliminary experiments indicate a possible trend for integrating physics-aware image formation with DUSt3R-type feed-forward reconstruction. However, several limitations remain: the current model does not yet achieve stable accuracy, real-world underwater validation has not been conducted, and computation costs remain high due to complex training procedures. Future work will focus on refining the network architecture, exploring DUSt3R-derived multi-view and high-fidelity extensions, accelerating computation, and validating the pipeline in real shallow-water environments. Additionally, integrating advanced rendering techniques may further improvethe refinement of 3D reconstruction.