3D Scanning and Reconstruction of Large Scale Environments

One task in the field of virtual and augmented reality is the acquisition of 3D environmental scenes which can be used to integrate artificial three-dimensional objects. This is commonly done by using stereo camera systems for scanning the environment. The major drawback on these solutions is a high purchase cost for such systems. New technological solutions for three-dimensional scanning have emerged over recent years, such as Microsoft’s Kinect.
Therefore the aim in connection with the VR Laboratory of Aalborg University is to create 3D environmental scenes using low-cost depth scanning equipment. Connected with that task is the research on usability of low-cost equipment in terms of accuracy and noise susceptibility.
A variety of approaches has been analyzed and tested to solve each partial task ranging from calibration issues and pose tracking to 3D reconstruction, which are presented in this report. The final system uses a point based tracking approach realized with SURF feature extraction, a minimum-correlation feature matching and Gauss-Newton iteration-based transformation parameter extraction. The memorylimiting task of large scale point cloud storage is solved with a dynamically growing hybrid hash map. A per-frame image-based reconstruction algorithm is used for in-place augmented reality. Full scan reconstruction is done using a parallelized version of Marching Cubes.
As a result the current implementation does not meet needed real-time requirements, leaving certain space for SIMD and MIMD parallelization optimizations.
The system is limited by high noise susceptibility and the need for highly accurate feature points, in particular in point feature-poor environments. These limitations are tolerable regarding the low cost of the system.

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