# A New Sensor System for Accurate 3D Surface Measurements and Modeling of Underwater Objects
## 4 Discussion  
In this paper, we presented a new underwater 3D scanning device designed for offshore (up to 1000 m depth) inspection applications. The sensor captures the surface of objects continuously and builds up a 3D model of the scanned object in real time by moving along the object with velocities up to 0.7 m/s. The sensor was tested in a water basin under clear water measurement conditions. The presented results of the new structured-light-based underwater 3D sensor mark a milestone in the development of such sensor systems concerning the achievable measurement accuracy. Results of static measurements of the specimens show that the systematic measurement error is in the same range of accuracy as 3D air scanners of comparable measurement volume. Additionally, using the 3D error compensation function, the systematic error decreased to below 0.2 mm, which is approximately 1/5000 of the measurement volume dimension. This is more accurate than some comparable air scanners. All these results were obtained in clear water under near-optimal extrinsic conditions. Hence, these results show the high accuracy potential of the device and not the real performance in possibly harsh conditions in real inspection applications offshore. Compared to previously presented setups or devices based on the same 3D data generation principles [32; 33; 34], our new system provides a considerably larger measurement volume, longer object distance, and shorter exposure time (see Table 4). These features, together with the developed motion compensation method, make application with a fast continuously moving sensor possible. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Device** & **Measurement Volume** & **Measurement Distance** & **Exposure Time** \\ \hline Ref [32] & 0.5 m \(\times\) 0.4 m \(\times\) 0.4 m 1 & 1.0 \(\pm\) 0.20 m & not specified \\ \hline Ref [33] & 0.5 m \(\times\) 0.4 m \(\times\) 0.2 m 1 & 1.0 \(\pm\) 0.10 m & 0.8 \(\dots\) 10 s \\ \hline Ref [34] & 0.25 m \(\times\) 0.2 m \(\times\) 0.1 m & 0.4 \(\pm\) 0.05 m & 15 ms \\ \hline UWS & 0.9 m \(\times\) 0.8 m \(\times\) 0.8 m & 2.0 \(\pm\) 0.40 m & 1\(\dots\) 2.6 ms \\ \hline \hline \end{tabular} \({}^{\dagger}\) estimation according to the provided data in the cited paper.  
\end{table} 
Table 4: Parameters of the 3D scanner compared previous devices based on structured illumination. Figure 8: Photograph of the sensor, pipe, and specimen (**left**) and false-color representation of the modeling result after merging of the continuously recorded 3D data sets (**right**). The detected random errors are in the expected range. For precise measurements at high velocities, motion must be taken into account. The presented compensation via displacement in the image space already achieves a significant improvement in the application. This was shown by experiments with a traversing track in air and a gantry system for different speeds in water. Furthermore, the 3D output data, improved by motion compensation, allows for finer registration and thus the recording of more accurate 3D models. Experiments with a sensor velocity of 1 m/s must be conducted before offshore measurements can be performed. The main task in future work is the extension of our experiments to offshore measurements under typical application conditions. These experiments should also analyze the influences of water turbidity, temperature, and salinity. Scattering effects of the particles in the water should also be studied. In order to simulate application at a 1000 m depth before real offshore experiments, we plan to test the UWS sensor system in an appropriate compression chamber. Another task of future work is to realize an appropriate calibration method that can be effectively performed offshore, even under rough weather conditions. Conceptualization: C.B.-B. ; methodology: C.B.-B., C.M. and M.B. ; software: M.H., C.M. and M.B. ; validation: C.B.-B. ; formal analysis: C.B.-B. ; investigation: C.B.-B. and I.G. ; resources: I.G. and M.H. ; data curation: C.B.-B. and M.H.