QUALITY ANALYSIS ON RANSAC-BASED ROOF FACETS EXTRACTION FROM 
AIRBORNE LIDAR DATA 
     
  
Jixing Yan ^ *, Wanshou Jiang 5 Jie Shan ** 
? School Of Remote Sensing and Information Engineering, Wuhan University, 430079, Wuhan, China — (yjxsky; 
shanj)@whu.edu.cn 
? State Key Laboratory Of Information Engineering In Surveying, Mapping and Remote Sensing, Wuhan University, 
430079, Wuhan, China - jws(g)lmars.whu.edu.cn 
* School of Civil Engineering, Purdue University, 47907, West Lafayette, IN, USA- jshan@purdue.edu 
KEY WORDS: Analysis, Quality, Algorithms, Extraction, LIDAR, Building 
ABSTRACT: 
RANSAC algorithm is a robust method for model estimation. It is widely used in the extraction of geometry primitives and 3D 
model reconstruction. However, there has been relatively little comprehensive evaluation in RANSAC-based approach for plane 
extraction. In order to provide a reference for improving the quality on RANSAC-based approach for roof facets extraction or 
segmentation, this paper focuses on the quality analysis on classical RANSAC algorithm. Airborne LIDAR data from the test Area 1 
and Area 2 in Vaihingen (German) is used. 33 buildings (4 buildings with flat roofs and 29 buildings with slope roofs) extracted 
from LIDAR data are taken as input for planes extraction. Based on the characteristics of detected planar surfaces, planes can fall 
into several categories: non-segmented planes, over-segmented planes, under-segmented planes and spurious planes. Then, several 
causes for these quality problems are discussed. Some experimental results and analyses show that, considering spatial-domain 
connectivity, most of the quality problems of classical RANSAC algorithm can be improved. However, there are still many issues 
requiring in-depth research. Finally, some methods are suggested to solve these problems. 
1. INTRODUCTION 
As a direct method for collecting dense accurate 3D point 
clouds, Light Detection and Ranging (LIDAR) has become an 
important technology in topographic mapping and 3D city 
modelling. 3D building reconstruction is a strong focus of 3D 
city modelling, and much progress has been reported 
(Vosselman, 1999, Verma, 2006, Sampath, 2010). Among the 
reported studies, building reconstruction is usually based on the 
assumption that a building is a polyhedral model, which 
consists of plane primitives. Consequently, the procedure of 
building roof reconstruction can be decomposed into two main 
steps: 3D roof facets extraction and topology construction. To 
some extends, the quality of 3D building models mainly 
depends on the accuracy of 3D roof facets detection from 
building (Elberink, 2011). 
Generally, 3D roof facets extraction from LIDAR data involves 
several basic methods and techniques such as segmentation, 
classification and clustering (Sampath, 2010). Local surface 
normal, calculated from the neighbourhood 3D points, is taken 
as the most important feature for detecting planes from building 
roof. However, surface normal is sensitive to noise. In addition 
to uncertainty in the measure, LIDAR data will inevitably 
contains returns from parts of trees, antenna or electric wires 
over building roofs. Moreover, the approach for neighbourhood 
selection from unstructured LIDAR point clouds will also affect 
the calculation accuracy of surface normal. 
Another popular method for extracting roof facets from point 
clouds is the Random Sample Consensus (RANSAC) (Forlani, 
2006, Bretar, 2005, Kurdi, 2007) algorithm and 3D Hough 
  
* Corresponding author. Jixing Yan, yjxsky@whu.edu.cn. 
Transformation (Vosselman, 2001, Huang, 2011). Both of them 
are robust methods for estimation of the model parameters. 
Hough Transformation and its extensions can only be used to 
detect several 3D objects such as lines, planes, cylinders etc, 
while RANSAC approach is more all-purpose in the detection 
of geometry primitives. In addition, Hough Transformation is 
sensitive to segmentation parameters. However, both of them 
can lead to false or surplus planes when used in the extraction 
of roof facets from LIDAR data (Vosselman, 2001, Tarsha- 
Kurdi, 2007). 
In terms of roof facets extracted by RANSAC, there have been 
many qualitative descriptions in literatures but seldom of them 
provide a comprehensive evaluation in quality. In order to 
provide a reference for improvement, we focus on the quality 
analysis. Building roofs extracted from Airborne Laser scanner 
Data in Vaihingen test areas (Cramer, 2010) are used, and some 
experiments and quality problems are discussed. 
This paper is organized as follows. In Section 2, we introduce 
the classical RANSAC algorithm for plane extraction and give 
an overview of related work in roof facets extraction. In Section 
3, we introduce the test data and some experiments in classical 
RANSAC for plane extraction, then the experimental results are 
analysed. In Section 4, we draw a conclusion from this work. 
And some future work is discussed. 
2. RANSAC AND RELATED WORK 
The RANSAC (Random Sample Consensus) algorithm 
proposed by Fischler and Robert (Fischler and Robert, 1981) is 
a robust method for extract models from a data set. It is often