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This study rigorously evaluates the performance of Gaussian splatting techniques for 3D reconstruction in various underwater environments characterized by differing turbidity and illumination conditions. By systematically testing five publicly available systems under a unified protocol, the research reveals that performance is heavily influenced by environmental setup rather than architectural differences. Key findings indicate that clarity of water significantly impacts frame registration, with medium-blind splatting outperforming medium-aware systems under specific lighting conditions, challenging existing benchmarks in the field.
Water clarity can make or break 3D reconstruction performance, with medium-blind splatting outperforming advanced methods in murky conditions.
The underwater environment is challenging for 3D reconstruction, because particles suspended in the water scatter and diffuse light, turbidity varies, absorption depends on wavelength, and illumination is rarely uniform. Methods based on Gaussian splatting have generally been developed for conditions that allow good image quality, and have primarily been tested on relatively shallow water. This paper examines how well Gaussian splatting performs across publicly available underwater datasets representing different degrees of turbidity, loss of illumination, and colour attenuation, together with an industrial survey. Five systems with public code are run under one protocol, with shared poses, initialisation, budget, and evaluator, to establish their relative advantages, disadvantages, and limitations. What these methods can do turns out to depend more on the setup than on the architecture. Water clarity binds upstream of rendering, since structure-from-motion registers 99.5 \% of frames in clear water and 0.0 \% at 12 NTU. Illumination geometry decides whether a medium model helps at all: under an artificial light that moves with the camera, medium-blind splatting beats both medium-aware systems. On the survey the benchmark's photometric leader comes last, beaten on geometry by a restoration pre-pass in front of vanilla 3DGS---and none of it is visible in the scores the field reports. Scene builds, per-run configurations, and evaluation code are released at https://github.com/olayasturias/uw3dgs