A NOVEL APPROACH FOR SEGMENTATION OF AIRBORNE LASER SCANNING 
POINT CLOUD LOCATED ON ROOF STRUCTURE 
Hongchao Fan *^ * 
* Dept. of Surveying and Geolnfomatic, Tongji University, Sipinglu 1239, 200092 Shanghai 
? Chair of GIScience, University of Heidelberg, Berlinerstr.48, 69120 Heidelberg 
Commission III, WG III/2 
KEY WORDS: 3D building, Segmentation, laser scanning, roof reconstruction 
ABSTRACT: 
This paper is addressed to an approach for segmenting airborne laser scanning point cloud. It aims to detect and cluster point cloud 
of an input roof into several segments that are corresponding to plane patches of the roof. The algorithm is developed based on the 
assumption that every non-flat roof can be decomposed into a set of gabled and/or hipped roof. The process detects roof ridges at 
first, and then detects points along each roof ridge. The experiments shows high efficiency and low error segmentation of the 
algorithm. 
1. INTRODUCTION 
Recent technological advances such as aerial photogrammetry, 
laser scanning, terrestrial measurement, 3D computer graphics 
etc. have greatly eased data acquisition, construction and 
visualization of detailed 3D building models. Hence 3D 
building reconstruction has been an active research topic for 
almost two decades. 
In general, approaches for 3D building reconstruction can be 
distinguished between image-based and point cloud based. 
Most  image-based approaches have focused on the 
reconstruction of specific building models: rectilinear shapes 
(Roux and McKeown, 1994; Noronha and Nevatia, 1997; Chen 
et al. 2004), flat roofs (Lin et al. 1994; Jaynes et al. 1997; 
Noronha and Nevatia, 2001; Shi et al. 2011) or parametric 
models (Fischer et al, 1998; Suveg and Vosselman, 2002; 
Hammoudi and Dornaika, 2011). But building roofs reveal a 
hug variety in structure and shape. Therefore, many of them fall 
beyond the predefined models. 
The image-based approach has the advantage of extracting 
outlines of roofs. It is hence suitable for detection roofs with 
simple shape or structure, e.g. flat roof, shed roof and simply 
gabled or hipped roof. But it can fragment or miss the line 
segments inside of the outlines, due to low contrast, occlusions, 
and bad perspectives. Therefore, it is not appropriate for roofs 
with complicated shape and structure, e.g. multi-gabled or 
hipped roof, or roofs with dormer windows. 
In the point cloud based approach, building roofs are measured 
by LIDAR (LIght Detecting and Ranging) with 3D geometries 
directly and represented as a number of 3D points. Building 
detection and construction from LIDAR data has also been 
studied by several researchers. Most methods start by 
converting the LIDAR point cloud to a depth image (Vosselman 
1999; Alharthy and Bethel 2002; You et al. 2003) and then use 
well known image segmentation techniques to detect buildings 
as rectilinear shapes. In further, a detailed literature review 
  
* Corresponding author. hongchao.fan@geog.uni-heidelberg.de 
96 
about the various approaches to extract buildings using imagery 
and LIDAR data can be found in Hu et al. (2003). 
On the other hand, several approaches are raised for 3D roof 
reconstruction by operating the 3D LIDAR data directly. In the 
early stage, most of the approaches (Weidner and Férstner, 
1995; Baillard and Zisserman, 1999; Nevatia and Price, 2002; 
Vinson and Cohen, 2002; Kim and Nevatia, 2004) reconstruct 
polyhedral roof structures based on determination of model 
primitive that will fit a dense and detailed 3D roof point cloud 
best. 
In recent years, some researchers tried to reconstruct roof 
structures without model primitives, since many roof structures 
go beyond the types of model primitives. The most common 
approach for the point segmentation is based on the assumption 
that a roof can be decomposed into several plane segments. 
Dorninger and Pfeifer (2008) give a comprehensive study on 
3D roof reconstruction based on Laser scanning point cloud and 
present an automated for point segmentation. In their work, 
Hebel and Stilla (2011) clustered the point cloud according to 
the Local Principal Component Analysis (PCA) and made the 
segmentation by method of region growing; finally, the planes 
are detected and fitted in each segment using the RANSAC 
techniques. But these approaches seem computation consumed, 
because all points have to be involved in iteration of the seed 
cluster determination. 
In this work, an automatic approach for point segmentation is 
proposed by detecting roof ridges first of all. It is based on the 
assumption that individual roof structures can be modeled 
properly by a composition of a set of planar faces. Although 
building roofs (especially in European countries) reveal a hug 
variety in structure, they can be categorized into two types: flat 
roofs and non-flat roofs. The process begins from the initial step 
for judging the types of the input roof point cloud and consists 
thereafter of four steps: (i) in the initial step, it is judged 
whether the input roof is a flat roof or non-flat roof; (ii) in the 
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