Quality control and project monitoring are topics of interest in the field of architectural-engineering-construction/facility management. The need to automatise the process and analyze data acquired with terrestrial laser scanners leads to optimum ways to perform the data acquisition guaranteeing the quality standards. This paper is focussed on the quantitative evaluation of a column detector based on the generalized hough transform (GHT) and the circle hough transform (CHT). Several quality conditions are considered, with the main goal to be able to determine the minimum requirements needed to be established during the data surveying. Elements such as completeness, point density, noise, and accuracy are simulated in laboratory conditions to perform the evaluation. Size and center position of round and rectangular cross-section columns are analyzed, reaching to a final conclusion that columns must be acquired with at least 30–40% of completeness. Noise effect is centered in the position of the column applying an error between 4 and 8 pixels. Point cloud resolution does not affect the results, whereas the projected image is stable and well distributed. The evaluation also showed a better performance of the Hough Transform on round than rectangular cross-section columns, primarily because the last ones are more sensitive to the amount of sides acquired and completeness values.

This manuscript proposes an algorithm for the reconstruction of the cube armoured rubble mound breakwaters’ geometry from incomplete lidar point clouds in order to perform structural monitoring over time. Rubble mound breakwaters are critical structures used in the protection of beaches and ports. The constant wave actions result in the degradation of these defences, causing catastrophic coastal damage. Early detection of the degradation of the breakwaters is a key topic to prevent disasters. Remote-sensing techniques such as lidar and photogrammetry contribute to the monitoring of civil engineering structures. The reconstruction of cube armoured breakwaters relies on normal-vector segmentation and a priori cube’s properties. For the successful modelling of the armour units, two options are available, the first one requires three perpendicular planes of the cube to be scanned and segmented, while for the second, only two planes are required. The resulting models were compared to ground truth handmade delineation of 1.25 m side length cubed armour units, leading to an accuracy range of 7–15 cm. Besides that, the precision results are constrained to 2.7–9.9 mm for the three plane-based reconstruction and 7.2–14.5 mm for the two plane-based one. The reconstructed armour units are well defined and suitable to be used as a 3D computer-aided design (CAD) model for monitoring breakwaters.

Acquisition of large scale scenes, frequently, involves the storage of large amount of data, and also, the placement of several scan positions to obtain a complete object. This leads to a situation with a different coordinate system in each scan position. Thus, a preprocessing of it to obtain a common reference frame is usually needed before analysing it. Automatic point cloud registration without locating artificial markers is a challenging field of study. The registration of millions or billions of points is a demanding task. Subsampling the original data usually solves the situation, at the cost of reducing the precision of the final registration. In this work, a study of the subsampling via the detection of keypoints and its capability to apply in coarse alignment is performed. The keypoints obtained are based on geometric features of each individual point, and are extracted using the Difference of Gaussians approach over 3D data. The descriptors include features as eigenentropy, change of curvature and planarity. Experiments demonstrate that the coarse alignment, obtained through these keypoints outperforms the coarse registration root mean squared error of an operator by 3 - 5 cm. The applicability of these keypoints is tested and verified in five different case studies.