Registration of terrestrial laser scanning data using planar patches and image data

Dold, Christoph; Brenner, Claus

doi:10.15488/3750

Cited by 34 publications

(1 citation statement)

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“…Some methods look interesting since they improve the efficiency of the feature alignment process. Dold et al [24] introduced a technology that identifies the 3D planar patches of the mesh and combines them by using a constrained search technique so that the matching combinations are reduced. Xu et al [25] proposed an automatic strategy for aligning planar patches based on the voxelization of the point cloud.…”

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confidence: 99%

Fast and Accurate Registration of Terrestrial Point Clouds Using a Planar Approximation of Roof Features

et al. 2022

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3D reconstructed models are becoming more diffused daily, especially in the Cultural Heritage field. These geometric models are typically obtained from elaborating a 3D point cloud. A significant limit in using these methods is the realignment of different point clouds acquired from different acquisitions, particularly for those whose dimensions are millions of points. Although several methodologies have tried to propose a solution for this necessity, none of these seems to solve definitively the problems related to the realignment of large point clouds. This paper presents a new and innovative procedure for the fine registration of large point clouds. The method performs an alignment by using planar approximations of roof features, taking the roof’s extension into account. It looks particularly suitable for the alignment of large point clouds acquired in urban and archaeological environments. The proposed methodology is compared in terms of accuracy and time with a standard photogrammetric reconstruction based on Ground Control Points (GCPs) and other ones, aligned by the Iterative Closest Point method (ICP) and markers. The results evidence the excellent performance of the methodology, which could represent an alternative for aligning extensive photogrammetric reconstructions without the use of GCPs.

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confidence: 99%