International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B3, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
    
  
  
PRIMITIVE-BASED 3D BUILDING RECONSTRUCTION METHOD TESTED BY 
REFERENCE AIRBORNE DATA 
W. Zhang^* *, Y. Chen, K. Yan*, G. Yan*, G. Zhou" 
? State Key Laboratory of Remote Sensing Science, Beijing Key Laboratory of Envorionmental Remote Sensing and 
Digital City, School of Geography, Beijing Normal University, 100875 Beijing, China - (wumingz, cym bnu, 
201121170050, gjyan)@bnu.edu.cn 
® GuangXi Key Laboratory for Spatial Information and Geomatics, Guilin University of Technology, 541004 Guilin, 
China, — (zgq) @glite.edu.cn 
Commission III, WG III/4 
KEY WORDS: Building Reconstruction, LiDAR, Optical Imagery, Primitive-based, Accuracy Evaluation 
ABSTRACT: 
Airborne LiDAR data and optical imagery are two datasets used for 3D building reconstruction. By study of the complementarities 
of these two datasets, we proposed a primitive-based 3D building reconstruction method, which can use LiDAR data and optical 
imagery at the same time. The proposed method comprises following steps: (1) recognize primitives from LiDAR point cloud and 
roughly measure primitives’ parameters as initial values, and (2) select primitives’ features on the imagery, and (3) optimize 
primitives’ parameters by the constraints of LiDAR point cloud and imagery, and (4) represent 3D building model by these 
optimized primitives. Compared with other model-based or CSG-based methods, the proposed method has some advantages. It is 
simpler, because it only uses the most straightforward features, i.e. planes of LiDAR point cloud and points of optical imagery. 
And it can tightly integrate LIDAR point cloud and optical imagery, that is to say, all primitives’ parameters are optimized with all 
constraints in one step. Recently, an ISPRS Test Project on Urban Classification and 3D Building Reconstruction was launched, 
two datasets both with airborne LiDAR data and images are provided. The proposed method was applied to Area 3 of Dataset 1 
Vaihingen, in which there are some buildings with plane roofs or gable roofs. The organizer of this test project evaluated the 
submitted reconstructed 3D model using reference data. The result shows the feasibility of the proposed 3D building reconstruction 
method. 
1. INTRODUCTION 
3D reconstruction of buildings is an important approach to 
obtain the 3D structure information of buildings, and has been 
widely used in the applications of telecommunication, urban 
planning, environmental simulation, cartography, tourism, and 
mobile navigation systems. It has been the major topic of 
photogrammetry, remote sensing, computer vision, pattern 
recognition, surveying and mapping. Traditionally, 
photogrammetry is the primary approach for deriving geo- 
spatial information through the use of multiple optical images. 
Optical imagery has sharp and clear edges, so the 3D 
information derived from photogrammetric measurements 
consists of accurate metric and rich descriptive object 
information (Mikhail et al., 2001). But it is hard to obtain 
dense 3D points on the building's surface because of the 
matching problem at the homogeneous or occluded places. Also 
because of matching problem, it is hard to generate 3D 
building model automatically by photogrammetry (Schenk and 
Csatho, 2002). 
Since it was introduced in the 1980s, as a promising method, 
Light Detection And Ranging (LiDAR) technology is used in 
the applications of acquiring digital elevation data. Because 
LiDAR technology is fully automated for generating digital 
elevation data, many researchers have paid attention to the 
  
* Corresponding author. 
technology and its applications (Arefi, 2009; Mayer et al., 2008; 
Rottensteiner and Briese, 2002). Although LiDAR point cloud 
has dense 3D points, these points are irregularly spaced, and 
don't have accurate information regarding breaklines such as 
building boundaries. Thus, the reconstructed 3D building's 
model is not very accurate (the accuracy depends on the points 
density), not only the shape but also the position of the 
building. Obviously, to generate a more accurate 3D building 
model using LiDAR point cloud, the help of other datasets with 
accurate boundaries is necessary. 
Both ground plan and optical imagery satisfy this requirement. 
Compared with ground plan, optical imagery has the 
advantages of easy availability and up-to-date state. A variety 
of research has been conducted using LiDAR point cloud and 
optical imagery, whatever data-driven or model-driven 
approaches (Habib, 2009; Kim, 2008; Tarsha-Kurdi et al, 
2007; Wang, 2008). The existing methods have some 
drawbacks. Firstly, most of these methods use edges as the 
features to connect LiDAR point cloud and optical imagery, the 
data processing procedure is complex due to the edge detection, 
filtering, combination and other operations. Secondly, the 
LiDAR point cloud and optical imagery are often processed 
respectively, and then the results are combined simply.