Nowadays, Unmanned Aerial Systems (UASs) are a wide used technique for acquisition in order to create buildings 3D models, providing the acquisition of a high number of images at very high resolution or video sequences, in a very short time. Since low-cost UASs are preferred, the accuracy of a building 3D model created using this platforms must be evaluated. To achieve results, the dean's office building from the Faculty of “Hydrotechnical Engineering, Geodesy and Environmental Engineering” of Iasi, Romania, has been chosen, which is a complex shape building with the roof formed of two hyperbolic paraboloids. Seven points were placed on the ground around the building, three of them being used as GCPs, while the remaining four as Check points (CPs) for accuracy assessment. Additionally, the coordinates of 10 natural CPs representing the building characteristic points were measured with a Leica TCR 405 total station. The building 3D model was created as a point cloud which was automatically generated based on digital images acquired with the low-cost UASs, using the image matching algorithm and different software like 3DF Zephyr, Visual SfM, PhotoModeler Scanner and Drone2Map for ArcGIS. Except for the PhotoModeler Scanner software, the interior and exterior orientation parameters were determined simultaneously by solving a self-calibrating bundle adjustment. Based on the UAS point clouds, automatically generated by using the above mentioned software and GNSS data respectively, the parameters of the east side hyperbolic paraboloid were calculated using the least squares method and a statistical blunder detection. Then, in order to assess the accuracy of the building 3D model, several comparisons were made for the facades and the roof with reference data, considered with minimum errors: TLS mesh for the facades and GNSS mesh for the roof. Finally, the front facade of the building was created in 3D based on its characteristic points using the PhotoModeler Scanner software, resulting a CAD (Computer Aided Design) model. The results showed the high potential of using low-cost UASs for building 3D model creation and if the building 3D model is created based on its characteristic points the accuracy is significantly improved.

In recent years non-metric digital cameras have known a great development, being used in wide applications, such as: real time monitoring and preservation of cultural heritage, the shoreline dynamics study, the wave movement for costal protection, soil erosion, etc. Clasic photogrammetry with the use of metric cameras are still of great use in areas such as architecture and environmental imaging. An old problem occurs when we want to extract metric information from our environment using closerange photogrammetry, with metric or non-metric calibrated cameras. There are many methods for this process that have been developed in some studies, by several authors. In this paper we present the Heikkilä and Silven method to determine the parameters of the UMK 10/1318 terrestrial photogrammetric camera and the parameters of a Canon EOS Rebel XSi/450D digital camera. To achive the proposed goal, a 3D calibration object was made on which, 42 points with known 3D coordinates were marked. The 3D coordinates were measured with the help of a Coordinate Measuring Machine (CMM) produced by Aberlink, with a 2µm precision. Through this calibration process the standard projective parameters of effective focal length and principal point along with radial and tangential distortion coefficients were determined. The purpose of this work is to make a first step in the analysis of the degree of confidence when using a non-metric camera to reconstruct an object in 3D.

ABSTRACT:The result of the terrestrial laser scanning is an impressive number of spatial points, each of them being characterized as position by the X, Y and Z co-ordinates, by the value of the laser reflectance and their real color, expressed as RGB (Red, Green, Blue) values. The color code for each LIDAR point is taken from the georeferenced digital images, taken with a high resolution panoramic camera incorporated in the scanner system. In this article I propose a new algorithm for the semiautomatic texture generation, using the color information, the RGB values of every point that has been taken by terrestrial laser scanning technology and the 3D surfaces defining the buildings facades, generated with the Leica Cyclone software. The first step is when the operator defines the limiting value, i.e. the minimum distance between a point and the closest surface. The second step consists in calculating the distances, or the perpendiculars drawn from each point to the closest surface. In the third step we associate the points whose 3D coordinates are known, to every surface, depending on the limiting value. The fourth step consists in computing the Voronoi diagram for the points that belong to a surface. The final step brings automatic association between the RGB value of the color code and the corresponding polygon of the Voronoi diagram. The advantage of using this algorithm is that we can obtain, in a semi-automatic manner, a photorealistic 3D model of the building.

This article presents (comparatively) the methodology for creating a 3D model of the urban area, based on terrestrial laser scanner, traditional technologies of terrestrial photogrammetry and aerial images. We are reviewing the data sources, their preliminary processing to be brought in a common system and the software used for this purpose. The case study presents the comparative results obtained using the methods listed above. To obtain the 3D models with terrestrial laser scanner systems have been used the dates achieved with ScanStation 2. To obtain the 3D model drawn by traditional photogrammetric methods using the UMK terrestrial camera, have been used auxiliary dates from topographic measurements with GPS systems and total stations as well as the current topographic plans. For the 3D model creation based on the dates taken with aerial digital cameras, were used the aerial images, taken with the ADS40 photogrammetric aerial camera. The comparative study between the three methods was accomplished by analyzing the object space representation fidelity, the precision of the 3D models obtained by comparison of the distances, areas and volumes, comparing the execution time and execution costs. The article presents the conclusions, the advantages and disadvantages of the three technologies based on the criteria listed above.

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