THESIS
2020
ix, 59 pages : illustrations ; 30 cm
Abstract
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
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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.
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