Creating a methodology to more objectively measure the performance of reconstruction algorithms for large urban objects generated from low detailed complete ground truth models

Abstract

In this thesis we present a new idea to objectively assess reconstruction algorithms. Because it is not feasible to completely scan a high-detailed ground truth mesh of large urban objects, the performance of the reconstructed meshes can therefore not be measured objectively. To solve this, we present a new mesh evaluation methodology that can more objectively assess the quality of the generated mesh based on a low detailed ground truth mesh. We achieved this by creating a synthetic dataset based on low-detailed models of large urban buildings and using this as a ground truth mesh and input data for the reconstruction algorithms. Thanks to our new methodology, we were able to compare the output mesh and ground truth mesh using a wireframe model of the meshes. This allows us to give a more objective score to the results without having to look at entire model, which is the usual method. The results of this thesis show that the new methodology has potential to be used for creating a new benchmark, and it opens a new door to using more readily available objects that could not be used before.