The Impact of the Calibration Method on the Accuracy of Point Cloud Reconstruction from Underwater RGB and Multispectral Images

Accurate photogrammetric reconstruction of underwater areas requires careful selection of camera calibration strategies. However, evaluation of calibration methods for consumer-grade cameras in very shallow environments is limited. In this study, five calibration strategies and three configurations of Brown’s model parameters were evaluated for underwater 3D reconstruction. This was achieved using a GoPro Hero 12 RGB camera and a Mapir Survey3W multispectral camera mounted on an unmanned surface vehicle (USV), which was placed in a controlled laboratory tank with a maximum depth of 0.5 m. For the GoPro camera, the frame model outperformed the fisheye model in underwater conditions despite the use of a wide-angle lens. The reduced Brown model (omitting parameters k3, k4, b1 and b2) produced the smallest mean signed M3C2 distance relative to the UAV-derived reference point cloud (6 mm). For the Mapir camera, the full Brown model produced the smallest mean signed M3C2 distance (38 mm). In both cases, in situ auto-calibration provided comparable or the best accuracy across all configurations, eliminating the need to acquire separate underwater calibration targets. Aerial calibration was found to be unsuitable for both cameras, regardless of the parameter configuration. The optimal calibration strategy was found to depend on the type of camera, indicating that it cannot be assumed a priori.