Integrating complementary information for photorealistic representation of large-scale environments

A wealth of datasets from different sensors exists for environment representation. The key observations of this thesis are that the different datasets are complementary and that fusing information from complementary datasets reduces errors in processing each dataset. In addition, a fusion method benefits from the merit of each dataset, hence helps us to represent large-scale environments in an efficient and accurate way.