3D point clouds simplification based on geometric primitives and graph-structured optimization

We present a new method for the simplification of 3D point clouds for digital twin city models. Such data usually contains a large amount of redundant information, noise and outliers. This implies that most of subsequent processing tasks are costly both in terms of processing times and hardware infrastructure. The core idea of this paper is that most of the objects present in such scenes can be approximated as a combination of simple geometric primitives. This approximation can, in turn, be simplified by keeping only points and edges that effectively describe the shape of the input scene. Our main contribution is a formulation of the approximation of 3D point clouds with simple geometric primitives as a global optimization problem. We then introduce a new algorithm to efficiently solve this problem with a graph-cut-based approach. We measure the performances of our approach against state-of-the-art methods by comparing the geometric quality of the approximations with the amount of information needed to represent the simplified models. The evaluation of our approach is done on Mobile Laser Scanning (MLS) acquisitions in urban areas. Test areas include single objects and street portions to show the adaptability of our method to various geometries. We show improvements both in terms of geometrical error and final model size.