Paper Info
Title
Ridge based curve and surface reconstruction
Abstract
This paper presents a new method for reconstructing curves and surfaces from unstructured point clouds, allowing for noise in the data as well as inhomogeneous distribution of the point set. It is based on the observation that the curve/surface is located where locally the point cloud has highest density. This idea is pursued by a differential geometric analysis of a smoothed version of the density function. More precisely we detect ridges of this function and have to single out the relevant parts. An efficient implementation of this approach evaluates the differential geometric quantities on a regular grid, performs local analysis and finally recovers the curve/surface by an isoline extraction or a marching cubes algorithm respectively. Compared to existing surface reconstruction procedures, this approach works well for noisy data and for data with strongly varying sampling rate. Thus it can be applied successfully to reconstruct surface geometry from time-of-flight data, overlapping registered point clouds and point clouds obtained by feature tracking from video streams. Corresponding examples are presented to demonstrate the advantages of our method.
Year
Venue
Keywords
2007
Symposium on Geometry Processing
density function,point cloud,surface geometry,noisy data,registered point cloud,point set,existing surface reconstruction procedure,time-of-flight data,unstructured point cloud,differential geometric analysis,surface,solid
Field
DocType
Citations 
Surface reconstruction,Regular grid,Computer science,Marching cubes,Sampling (signal processing),Geometric analysis,Geometry,Point cloud,Local analysis,Probability density function
Conference
6
PageRank 
References 
Authors
0.47
32
2
Name
Order
Citations
PageRank
Jochen Süßmuth113811.30
Günther Greiner259880.74