Make it Complete : Surface Reconstruction Aided by Geometric Primitives

Abstract: Being able to reconstruct real-world environments into digital 3D models is something that has many different types of interesting applications. With the current state of the art, the results can be very impressive, but there is naturally still room for improvements. This thesis looks into essentially two different parts. The first part is about finding out wether it is feasible to detect geometric primitives, mainly planes, in the initially reconstructed point cloud. The second part looks into using the information about which points have been fitted to a geometric primitive to improve the final model. Detection of the geometric primitives is done using the RANSAC-algorithm, which is a method for discovering if a given model is present in a data set. A few different alternatives are evaluated for using the information about the geometric primitives to improve the final surface. The first option is to project points onto their identified shape. The second option is to remove points that have not been matched to a shape. The last option is to evaluate the possibility of changing the weights of individual points, which is an alternative available in the chosen surface reconstruction method. The detection of geometric primitives shows some potential, but it often requires manual intervention to find correct parameters for different types of data sets. As for using the information about the geometric primitives to improve the final model, both projecting points and removal of non-matched points, does not quite address the problem at hand. Increasing the weights on matched points does show some potential, however, but is still far from being a complete method. A small part of the thesis looks into the possibility of automatically finding areas where there are significant differences between the initial point cloud and a reconstructed surface. For this, hierarchical clustering is used. This part is however not evaluated quantitatively