@InProceedings{10.1007/978-981-16-5576-0_6,
author="Yadav, Sunil Kumar
and Skrodzki, Martin
and Zimmermann, Eric
and Polthier, Konrad",
editor="Cheng, Jin
and Dinghua, Xu
and Saeki, Osamu
and Shirai, Tomoyuki",
title="Surface Denoising Based on Normal Filtering in a Robust Statistics Framework",
booktitle="Proceedings of the Forum ``Math-for-Industry'' 2018: Big Data Analysis, AI, Fintech, Math in Finances and Economics",
year="2021",
publisher="Springer Singapore",
address="Singapore",
pages="103--132",
abstract="During a surface acquisition process using 3D scanners, noise is inevitable and an important step in geometry processing is to remove these noise components from these surfaces (given as point set or triangulated mesh). The noise removal process (denoising) can be performed by filtering the surface normals first and by adjusting the vertex positions according to filtered normals afterward. Therefore, in many available denoising algorithms, the computation of noise-free normals is a key factor. A variety of filters have been introduced for noise removal from normals, with different focus points like robustness against outliers or large amplitude of noise. Although these filters are performing well in different aspects, a unified framework is missing to establish the relation between them and to provide a theoretical analysis beyond the performance of each method.",
isbn="978-981-16-5576-0"
}