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dense corresponding pixel matching between epipolar images. The method takes the independent physical properties of RGB information into consideration. The RGB differences from
As can be clearly seen from figure 6, the contribution of error due to laser scanner and orientation parameters increases as the moving object goes further away.. While, the effect
It clearly shows how the highly dense point cloud of the TLS survey allows the user to have a continuous visualization of the transversal profile in order to
The correction matrix (figure 5) for the camera was determined on the basis of images of the integrating sphere (FFC_UK).. The images of the LED backlight were corrected us- ing
The adoption of laser scanner three-dimensional techniques is able to achieve a geo-morphometric database that has shown to be particularly The International Archives of
Finally more realistic example data, scanned walls of a room with different orientation and different distance with respect to the laser scanner (Fig. 12), were corrected
Figure 6: VisualSFM and SURE used to generate a dense point cloud of Vari Hall from the sUAMS' video imagery.. Firstly the point clouds were segmented into groups of
The acquired point cloud data was georeferenced to ETRS-TM35FIN coordinate system by integrating the measured laser points, trajectory data and synchronization
