Surface reconstruction from unorganized point cloud data using incremental Delaunay triangulation
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3D Surface reconstruction is a widely researched field which involves the various aspects of data capture from sources like Laser Scanners and other Infra-red devices. The most common form of representing this data obtained from such sources is to use Triangulated meshes which are computationally expensive and require more complex data structures to be stored. Since with the current advent of technology, increasing complex 3D models are being created as well as scanned from various sources like statues, it becomes extremely important to handle such huge information in Real-time and manipulate such data to provide results in real-time to the end user. In this thesis, an incremental Delaunay triangulation is being used which can be used to triangulate the concave regions of the point cloud. The advantage of this process is that a constrained Delaunay triangulation doesn't have to be used. The triangles from Delaunay Triangulation are selected on an incremental basis after rejecting the triangles which lie in the interior of the body. The region growing process then grows the triangulation to the whole body. The focus of this work is to develop a framework and algorithm which is intuitive in nature. The triangles which do not lie on the surface are pruned by checking intersection of triangles with interior-exterior nodes.