Using airborne LiDAR data and optical imagery, we proposed 
a primitive-based 3D building reconstruction method to 
overcome the problems mentioned above (Zhang et al., 2011). 
Two datasets are tightly integrated, and the accurate 3D 
building model can be acquired by the straightforward and 
simple features. 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 (Rutzinger et al., 2009). The result shows the 
feasibility of the proposed 3D building reconstruction method. 
The organization of this paper is as follows. In section 2, the 
proposed primitive-based method is described in detail, 
including motivation, workflow, and explanation of some 
crucial steps. In section 3, first is the description of test data, 
followed by evaluating result and some discussions. Finally, 
we draw the conclusion and identify the work of near future. 
2. METHODOLOGY 
2.1 Selection of Reconstruction Method and Features 
In this section, two crucial points will be explained, i.e., the 
selection of method and features for building 3D reconstruction. 
There are two reasons for the selection of primitive-based 
method to reconstruct 3D building model. 
Firstly, LiDAR point cloud has dense 3D points, but these 
points are irregularly spaced, and don’t have accurate 
information regarding breaklines such as building boundaries. 
On the contrary, optical imagery has sharp and clear edges, but 
it is hard to obtain dense 3D points on the building’s surface. 
In order to reconstruct 3D building model by integration of 
LiDAR point cloud and optical imagery, the selected object 
must have clear edges and dense surface points at the same 
time. Obviously, primitives, for example, box, gable-roof and 
hip-roof can satisfy this requirement. Suitable primitives will 
“glue” LiDAR point cloud and optical imagery. 
Secondly, from the point view of computation, primitive-based 
representation of 3D building model has less parameters. For 
example, to represent a box, 3 parameters (width, length and 
height) are used to represent the shape; together with 3 
parameters for position and 3 parameters for orientation, totally 
9 parameters are enough to determine the shape and locate the 
box in 3D space. So the solution can be calculated easily and 
robustly. 
For the selection of features, it is crucial because it affects the 
complexity of the process and the accuracy of the reconstructed 
3D building model. As we have seen, LIDAR point cloud and 
optical imagery have different characteristics, so different 
features will be selected for these two datasets. The features 
should be as straightforward and simple as possible, so that 
they can be easily located and accurately measured. Plane is 
the feature that we selected for LiDAR point cloud, and corner 
is the feature that we selected for optical imagery. Using these 
straightforward and simple features, the computational 
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 
   
procedure is simplified, and the result can be obtained 
precisely and robustly. 
Because of above reasons, we select primitive-based method to 
reconstruct 3D building model, and plane feature for LiDAR 
point cloud and corner feature for optical imagery. 
2.2 Hip-roof Primitive 
As mentioned above, the main roof types in test area are plane 
roofs and gable roofs. These two types of roofs can be regarded 
as the simplification of hip-roof. The hip-roof primitive used in 
this paper is shown in Fig. 1. The coordinates framework and 
the parameters are labelled. It can be seen that 6 parameters 
are used to define the shape of this hip-roof primitive. Further 
more, another 6 parameters define how a primitive is placed in 
3D space, 3 for position and 3 for orientation. 
  
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width : m 
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Figure 1. Hip-roof primitive 
2.3 Workflow 
Fig. 2 shows the workflow of this primitive-based 3D building 
reconstruction method. The numbers denote the order of 
processing. 
LiDAR point cloud IZ Optical imagery 
= E 
1 2 
2 ve 5 d 
Recognize primitives and Extract features 
measure initial parameters 
Corners of 
Initial values of buildings 
primitives 
3 
Compute features 
Corners of 
primitives 4 
4 
Optimize parameters 
3D buildings represented by primitives 
with optimized parameters 
  
  
*—  — N 
   
    
         
       
     
  
Extract features 
  
     
  
   
  
  
   
  
Planes of 
buildings