Comparing Aerial Photogrammetry and 3 D Laser Scanning Methods for Creating 3 D Models of Complex Objects

In the last decade, the evolution in computing power both in CPUs and GPUs has allowed the development of photogrammetry software that can turn a set of photos of an object, such as a building, into a 3D model. These software products compete with laser scanners, which are widely used throughout design, construction, and operations of infrastructure assets due to their fast acquisition time and remarkable accuracy. In this paper, we assess the accuracy of photogrammetry reconstruction by comparing outcomes when capturing and processing various scenes using ContextCapture software [4] and laser scanner generated point clouds.  When undertaking the protection or renovation of archeological ancient marvels, it is important to accurately survey and document sites to create detailed documentation. This documentation, which is typically done digitally, helps to ensure the fidelity of the future renovation. Equally important, by analyzing and researching the digital documentation, restoration teams can understand and replicate the techniques used for the initial construction. For years, laser scanners have been widely used to capture digital data for archeological sites because they are fast, versatile for surveying purposes, and are accurate within millimeters. However, the scanning process can be tedious, and the technology requires highly skilled, welltrained individuals in order to obtain a virtual 3D representation of the real world. In contrast, producing an accurate, 3D model using photogrammetry requires only a camera of reasonable quality. Photogrammetric software can then automatically build the model using multiple pictures of the same object.  Much has been written about photogrammetry recently, including: An evaluation of the photogrammetry process in laboratory conditions.  The first comparison of the technology using real scenes  An extensive review of optical 3D measurement sensors and 3D modeling techniques for cultural heritage use cases [4, 5 A review of the pipeline for 3D modeling from terrestrial data  In this paper, we compare 3D models reconstructed using ContextCapture software with a 3D model created from terrestrial LiDAR point clouds, with the goal of assessing 1 Hartley, R. I. and Zisserman, A.,Multiple View Geometry in Computer Vision, 2nd ed.  2004. 2 Seitz, Steven M and Curless, Brian and Diebel, James and Scharstein, Daniel and Szeliski, Richard, A comparison and evaluation of multi view stereo reconstruction algorithms. Computer vision and pattern recognition, 2006. 3 Remondino, F, Heritage recording and 3D modeling with photogrammetry and 3D scanning. Remote Sensing, 3(6) 1104 1138. 4 Strecha, W. von Hansen, L. Van Gool, P. Fua, U. Thoennessen, On Benchmarking Camera Calibration and Multi View Stereo for High Resolution Imagery Computer Vision and Pattern Recognition, 2008. 5 El Hakim, S. F. Beraldin, J. A. Picard, M. and Godin, G. Detailed 3D reconstruction of large scale heritage sites with integrated techniques. Computer Graphics and Applications,  2004, 24(3) 21 29. 6 Remondino, F. El Hakim, S. Image based 3D Modeling: a Review. The Photogrammetric Record, Issue 21, Pages 269 291. the accuracy of the photogrammetric reconstruction. All of the datasets discussed were captured for a professional purpose, which allowed us to understand whether photogrammetry can be used professionally to digitally document the kinds of complex projects usually completed with laser scanners. The castle of Penne, located on the top of a steep hill in the south of France, was selected as the scene for the comparison. The pictures for the reconstruction were taken with a typical digital camera, and the LiDAR data was captured using a terrestrial laser scanner. Acquiring Site Data For the photogrammetry data collection process, Gerpho, a team of professional aerial photographers, took pictures of the castle from a plane at a distance of roughly 400 meters. They used a Nikon D800 camera with a 36MP full frame CMOS sensor and a Nikon 70 200 lens. A total of 249 pictures were taken all with a focal length ranging between 139 millimeters and 201 millimeters. The picture dimensions were 7,360 by 4,912 pixels.  1 provides an example of a typical picture captured of the castle. Figure 1 Boliden's current Aitik fleet. For the laser scanning data collection, Sompayrac Cianferani Prieu, a professional land surveyor, acquired a LiDAR point cloud that was then processed by Geovast 3D. The 3D model features approximately 1 billion points after data unification. The surveyor used a Leica HDS 7000 scanner, which has ranging errors within 2 millimeters. Forty six different scans were registered all with a maximum constraint of 6 millimeters deviation.  The entire point cloud, processed by Geovast 3D. Creating 3D Models Georeferencing was done using RTK and the TERIA network on 18 specific targets in order to precisely align the two models. 3D Model Creation Using a LiDAR Point Cloud Using traditional laser scanning, the castle structure was captured in three parts: The first part was the corner wall, with 4,991,330 points in the point cloud  The second piece of the structure captured was a wall and a rooftop viewed from the bottom of the tower of the castle. This point cloud contained 27,831,695 points  The third piece was another wall from the tower in a point cloud containing 9,802,203 points  Figure 3 Extract 1 Error ranges from 0 to 0.87 meters.  Extract 2 Error ranges from 0 to 0.71 meters.  Extract 3 Error ranges from 0 to 0.94 meters. 3D Reconstruction of Pictures Using ContextCapture The reconstruction of the traditional pictures into a 3D model was performed using ContextCapture  The parameters for the aerotriangulation process (for computing the orientation and position of the pictures) were set to the defaults, and the parameters for the 3D reconstruction were set to "Highest," the default preset.  was used to compare the 3D photogrammetric reconstruction and the LiDAR point cloud. The 3D photogrammetric model was set as the reference, and CloudCompare computed the distance from each point of the LiDAR point cloud to the surface mesh of the 3D model. The mesh (M) is made up of interconnected triangles (t) The distance between a point (p) and the mesh (M) is computed as: dist(p, M) minttriangles (dist(p, t) where the distance between a triangle and a point is defined by the distance between the plane containing the triangle and the point. Deviation Comparison No attempt was made to align the point cloud and the 3D model directly, both of which were georeferenced in the RGF93 CC43 coordinate system. Rather, the alignment was made using the seven control points.  An example of people "noise" in the laser scan. It is important to note that the photos and the laser point cloud were not captured at the same time. As a result, there are differences between the two data captures. For example, some objects have moved, and there is "noise" (for example, people) featured in the LiDAR data, but not the photographs  Also, objects like planks had been moved, and there are other outliers in the scan data due to the weather and various material properties. For these reasons, the comparison between the photogrammetric 3D model and the LiDAR data was made (see Figures 8 and 9) on the parts of the clouds that were the same between the two captures, enabling the elimination of noise and other artifacts such as moving objects. 7 CloudCompare, 3D point cloud and mesh processing software.  Distance between the point cloud and the mesh for Extract 3. The results of the deviation comparison are summarized in Table 1. CloudCompare provided the distance between the point cloud and the mesh, which was assimilated as an error. The columns give the average error, the median error, the smallest range containing 90 percent of the points, and the root mean square error.