ABSTRACT: The accurate perception of the environment of vessels is essential for the development of automated maritime systems and remote operation, as it is important to ensure a safe navigation in narrow fairways and in harbor areas. LiDAR (Light Detection and Ranging) scanners are widely used in object detection applications, as they produce dense 3D point clouds. However, due to their line-of-sight limitations, only partial views of objects are recorded, making a full shape reconstruction necessary for safe navigation and situational awareness. This paper presents a method to reconstruct full object dimensions from incomplete LiDAR measurements. We introduce an automated selection algorithm that chooses the most suitable model from segmented point clouds based on geometric characteristics, aiming to reconstruct full object shapes. To improve modeling accuracy for navigation, we evaluate three advanced models compared to the Box Model: Cylinder Model, L-Shape Box Fitting, and Elliptic Cylinder Fitting. The reconstruction accuracy is quantified using the root mean squared error (RMSE) by comparing the fitted models against ground-truth point clouds compiled from multi-view scans. Furthermore, we compare the error of our proposed selection method to the box model, providing insight into its advantages and limitations for maritime object modeling. Field tests with representative maritime objects from two harbor locations are presented. The results show that the choice of reconstruction method plays a key role in how accurately maritime objects can be modeled. Simple shapes such as buoys, pontoons and piles are well represented by basic models, whereas complex or irregular structures require more flexible reconstruction methods, such as Triangle Mesh. Adapting the modeling technique to object geometry reduces manual and computational effort while supporting reliable navigation and autonomous operation.

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