SMP-UWGS: Coupled Physics-Geometry Optimization for Scalable Multi-Partition Underwater 3D Reconstruction

We propose SMP-UWGS, an underwater reconstruction frame-work based on 3D Gaussian Splatting (3DGS) that couples scalablemulti-partition geometry with physical light transport modeling. Ourmethod introduces two key components: SMP-GAUSSIAN, a multi-partitionarchitecture enabling distributed optimization for large-scale scenes, andDPR-Net (Differentiable Physical Rendering Network), which combinesWaterParamPredict module for estimating water optical parameters witha Dual-Branch Differential Refinement (DBDR) module to model atten-uation and backscattering. With tailored loss functions, the frameworkjointly estimates water optical parameters and improves color fidelityand geometric accuracy. Experiments on public datasets show that SMP-UWGS achieves state-of-the-art efficiency while enabling scalable, high-fidelity underwater reconstruction, benefiting applications such as ecolog-ical monitoring, habitat mapping, and autonomous underwater robotics.