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An antique 300-year old wooden globe, preserved in the National Museum of Switzerland, had to be copied physically for deliveryto another party. For this purpose a 3D computer model had to be generated. A structured light system and two digital frame cameras were employed, and the generated datasets were integrated to obtain both the geometry and the texture of the model. This paper reviews the whole workflow from data acquisition to the final geometrical surface and textural information. The results of the processes are presented and discussed and some conclusions regarding the exploitation of the two mentioned techniques are given., International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVII (B5), ISSN:1682-1750, ISSN:2194-9034, ISSN:1682-1777

Cleaning laser scanner point clouds from erroneous measurements (outliers) is one of the most time consuming tasks that has to bedone before modeling. There are algorithms for outlier detection in different applications that provide automation to some extent butmost of the algorithms either are not suited to be used in arbitrary 3 dimensional data sets or they deal only with single outliers orsmall scale clusters. Nevertheless dense point clouds measured by laser scanners may contain surface discontinuities, noise and diffrentlocal densities due to the object geometry and the distance of the object to the scanner; Consequently the scale of outliers may varyand they may appear as single or clusters. In this paper we have proposed a clustering algorithm that approaches in two steps with theminimum user interaction and input parameters while it can cop with different scale outliers. In the first step the algorithm deals withlarge outliers (those which are very far away from main clusters) and the second step cops with small scale outliers. Since the algorithmis based on clustering and uses both geometry and topology of the points it can detect outlier clusters in addition to single ones. Wehave evaluated the algorithm on a simulated data and have shown the result on some real terrestrial point clouds. The results explainthe potential of the approach to cop with arbitrary point clouds and different scale erroneous measurements., International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVI (3/W52), ISSN:1682-1750, ISSN:2194-9034, ISSN:1682-1777

Outlier detection in laser scanner point clouds is an essential process before the modelling step. However, the number of points in the generated point cloud is in the order of million points, so (semi) automatic approaches are necessary. Having introduced the sources of outliers in typical laser scanner point clouds, an outlier detection algorithm using a density based algorithm is addressed. The algorithm is chosen due to its unconstrained behaviour to the preliminary knowledge of the scanned scene and the independency to the varying density of the points. The algorithm efficiency is assessed by a test on an aerial laser scanner point cloud. The assessment is done with respect to a DSM obtained by photogrammetric methods. The type I and type II errors are estimated and the results are reported. Some examples in terrestrial laser scanner point clouds are also presented and the behaviour of the algorithm on the data sets are shown and discussed. Although the algorithm dose not detect all outlier clusters, the detection of single outliers and even cluster outliers with lower density than a predefined value seems satisfactory, specially the results in the boundary of occlusions. Detection of such outliers is important because if they remain in the data set, they may cause modelling errors in the next modelling step., International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVI (5), ISSN:1682-1750, ISSN:2194-9034, ISSN:1682-1777