Iterative scan planning for automated 3D reconstruction
3D Scanning is one of the most important and interesting areas in modern Computer Vision research. 3D Scanning mainly deals with creating a virtual CAD/Polygonal mesh model of real object. For proper preservation and restoration of cultural objects, it is very important to have detailed information about the object in 3D. The current methods available to scan and represent the digital 3D model of such objects are very cumbersome and expensive. In this thesis, we present a method for Iterative scan planning for Automated 3D reconstruction of Cultural Heritage Objects. The main area we have focussed is automated 3D scanning. Primary goal is to implement automated scanning with minimum human intervention in the entire process. The method intended will be similar to the concept of Assembly line in production, where the automation and separation of individual tasks will help to speed up the process of 3D scanning. We have implemented a method similar to mass vector chain approach for iterative scanning. Our algorithm predicts evolution of 3D surface based on previously scanned surfaces. This idea coupled with several approaches from graph theory provides robust estimation for the surface evolution, which can be used in determining next pose. Another part of the thesis focuses on "Geometric lossy compression of 3D meshes using convex hulls". This method is entirely new proposed by the author. This method is based on use of ellipsoidal convex hulls to get lossy compression of the given geometric 3D mesh. The compressed version of mesh can later be used for multi-level or multi-resolution viewing and easy transportation over web. The use of ellipsoidal convex hulls combined with the method of ray-casting provides at least 35 percent compressions for the same number of vertices, although the original vertices are not retained. Instead they will be replaced by the new vertices obtained by the method of ray-casting onto the surface of given 3D mesh.
Dijon, Univ., Master Thesis, 2012