The advancement of modern remote sensing technology reduces the cost of acquiring three-dimensional information in the real-world. The information obtained is generally represented in discrete data points known as the point cloud. Many researchers focus on algorithms and technologies to extract the underlying topology supporting many interesting applications such as indoor navigation and heritage reconstruction. In this thesis, we focus on the thin and linear structures within the point cloud instead of surfaces and we propose an automatic pipeline taking raw point clouds of large-scale landscapes, which are generated by the multi-perspective image reconstruction algorithms. Our pipeline removes outlier, reduces redundancy, computes local features, and generate vectorization re...[ Read more ]

The advancement of modern remote sensing technology reduces the cost of acquiring three-dimensional information in the real-world. The information obtained is generally represented in discrete data points known as the point cloud. Many researchers focus on algorithms and technologies to extract the underlying topology supporting many interesting applications such as indoor navigation and heritage reconstruction. In this thesis, we focus on the thin and linear structures within the point cloud instead of surfaces and we propose an automatic pipeline taking raw point clouds of large-scale landscapes, which are generated by the multi-perspective image reconstruction algorithms. Our pipeline removes outlier, reduces redundancy, computes local features, and generate vectorization result of the linear structures. Moreover, to provide a standard vectorization result for evaluation purposes, we designed and implemented a manual tool allowing the user selection of the points of interest and generate vectorization results with proper visual feedback. A real-world problem of digitizing the location and shape information of the high-voltage powerlines is the main task of our pipeline and it provides a context for analyzing the correctness and effectiveness of each stage in our pipeline.