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Contribution Details

Type Bachelor's Thesis
Scope Discipline-based scholarship
Title A Point-Cloud Normal Surface Estimation Methods Comparision
Organization Unit
Authors
  • Alessio Brazerol
Supervisors
  • Renato Pajarola
  • Luciano Romero Calla
Language
  • English
Institution University of Zurich
Faculty Faculty of Business, Economics and Informatics
Date 2023
Abstract Text Point clouds are used in various algorithms in related fields, such as Visual Computing. Some of these algorithms require high-quality normals as input. Computing these surface normals is often the job of a different algorithm. Researchers have suggested vastly different algorithms over the years. Recent work focuses on using deep learning to estimate surface normals. It is important that these normal estimation algorithms perform well, as the other algorithms rely on their quality. This means the normal estimation algorithms need to be robust against various defects, including noise, outliers and differences in sampling density. Researches have proposed different measures to evaluate these normal estimation algorithms as well as how to produce synthetic test data to test against. Synthetic test data is especially useful as it provides ground truth normals to test against and can be constructed to contain a single or multiple defects with various strengths. Because of this, synthetic models are a valuable addition to real-world test data. In this paper, we evaluate four different normal estimation algorithms. This includes three regression based and one deep learning based method. We develop a tool to execute and compare the selected normal estimation algorithms. Our goal is to use good measures and cover multiple defects with our test data. In order to do this, we test the methods against various levels of noise and outliers. We use five different synthetic point cloud models and a real-world point cloud to test against. This allows us to identify the strengths and weaknesses of the tested methods.
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