ROBUST CYLINDER FITTING IN THREE-DIMENSIONAL POINT CLOUD DATA

Robust Cylinder Fitting in Three-dimensional Point Cloud Data

This paper investigates the problems of cylinder fitting in laser scanning three-dimensional Point Cloud Data (PCD). Most existing methods require full cylinder data, do not study the presence of outliers, and are not statistically robust. But especially mobile laser scanning often has incomplete data, as street poles for example are only scanned from the road. Moreover, existence of outliers i...

Diagnostic-robust Statistical Analysis for Local Surface Fitting in 3d Point Cloud Data

This paper investigates the problem of local surface reconstruction and best fitting for planar surfaces from unorganized 3D point cloud data. Least Squares (LS), its equivalent Principal Component Analysis (PCA) and RANSAC are the three most popular techniques for fitting planar surfaces to 3D data. LS and PCA are sensitive to outliers and do not give reliable and robust parameter estimation. ...

Fitting 3D Data with a Cylinder

Template Deformation for Point Cloud Fitting

The reconstruction of high-quality surface meshes from measured data is a vital stage in digital shape processing. We present a new approach to this problem that deforms a template surface to fit a given point cloud. Our method takes a template mesh and a point cloud as input, the latter typically shows missing parts and measurement noise. The deformation process is initially guided by user spe...

Automatic Three-Dimensional Point Cloud Processing for Forest Inventory

In this paper, we propose an approach that enables automatic, fast and accurate tree trunks segmentation from three-dimensional (3-D) laser data. Results have been demonstrated in real-time on-board a ground mobile robot. In addition, we propose an approach to estimate tree diameter at breast height (dbh) that was tested off-line on a variety of ground laser scanner data. Results are also prese...