filename     : Ozt15c.pdf
entry        : article
conference   : 
pages        : 100-106
year         : 2015
month        : July
title        : Making Sense of Geometric Data
subtitle     :
author       : A. Cengiz Öztireli
booktitle    : Computer Graphics and Applications
ISSN/ISBN    : 0272-1716
editor       : 
publisher    : IEEE
publ.place   : 
volume       : 35
issue        : 4
language     : English
keywords     : computer graphics;Internet;computer graphics;displays;geometric data;manifold surface reconstruction;printers;sensors;stochastic point patterns;unstructured point samples;Geomtric data;Image reconstruction;Manifolds;Noise measurement;Surface reconstruction;Surface treatment;MLS;RIMLS;computer graphics;geometric data;manifold surfaces;moving least squares;pair correlation functions;point distributions;point processes;robust implicit moving least squares
abstract     : Most data acquired from the real world is or can be interpreted as geometric in nature. Advanced and affordable sensors, printers, displays, and the Internet make geometric data increasingly important for many disciplines. Giving structure and meaning to this data has been one of the main challenges of computer graphics as well as other fields in the last few decades. The author's PhD thesis started as an effort to turn this massive amount of data into digitally meaningful representations useful for various applications in computer graphics and beyond. In turn, his work targets the problems of reconstructing manifold surfaces and stochastic point patterns from unstructured point samples.