CMRT09: Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms, and Evaluation 
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equipment was precisely installed by topometry. It allows the 
gathering of georeferenced 3D laser data with a very high density 
and also much information about the acquisition (see section 4). 
Figure 1: Visualization of the 3D point cloud of very high den 
sity. This raw data represents the building facade acquisition. The 
black line represents the trajectory of the laser sensor. 
The laser data are acquired under realistic conditions in dense 
urban environments. Moreover, the datasets are collected in a 
particular container related to the laser scanner, particularly in a 
range of 3D points. In this context, the data must be manipulated 
with much precaution. Here we will describe the main difficulties 
associated with 3D data. 
• The acquisition: The laser sensor data can be represented 
in two ways; either as a container with data organized lin 
early in temporal sequences of 201 points (data frames) or 
like a cloud of 3D points (georeferenced data). The condi 
tions of acquisition are very variable in realistic and dense 
urban environments. Mobile objects cause a thickening of 
the acquired cloud when the vehicle stops. Certain acquired 
points model an ephemeral surface and could be considered 
as erroneous points. Moreover, the density of the facades 
vary according to the speed of the vehicle. 
• The occlusions: pose a problem for the complete acquisi 
tion of building facades in urban environments. The occlu 
sions could be caused by two categories of obstacles, static 
or dynamic created by man-made and natural objects. The 
raw cloud may suffer from missing data due to the pres 
ence of pedestrians, trees, mobile and parked vehicles and 
many others objects (see figure 2). Alas, this inevitable phe 
nomenon affects the modeling process. 
• The laser reflectance: could cause confusions in the 3D 
data interpretation. Certain points don’t model a physical 
surface. This effect appears on a retroreflector surface. Ob 
servations sometimes show an aureole of points around road 
signs. These dispatched points represent erroneous data. 
Moreover, certain points model a different surface other than 
the surface of interest. Sometimes, the beam of the laser ei 
ther rebounds off of the outside of the window or it passes 
through the window and models the inside of the dwelling. 
These scattered points represent erroneous information for 
the facades modeling. In addition to this, other less frequent 
effects could arise due to poorly reflective surfaces. • 
• The redundancy of data: is due to many factors. The ac 
quisition is continuous even when the vehicle is stopped. 
We have adopted this strategy to facilitate the acquisition of 
a large area and to use data as common bases for our dif 
ferent projects. Therefore, raw laser data may contain many 
redundant frames. Moreover, due to sensor characteristics 
(orientation and linear scanning), we could sometimes have 
up to three acquisitions of the same facade part caused by 
the graining of the laser beam in the turns. The redundancy 
of data (points, frames, parts of the facade) presents an in 
convenience for the feature extraction techniques based on 
vote schemes or random trials. 
Figure 2: A street in the city of Paris. The building facade is 
partially occluded by trees and parked vehicles. 
Figure 3: Returned intensities of the 2D scans. The redundancy 
effect appears when the vehicle temporarily stops. The vehicle 
and the branches seem stretched. 
This brief description allows us to acknowledge several problems 
associated with the raw laser data. The 3D data should undergo 
several preprocessing steps before becoming exploitable. Thus 
we need a process robust to some outliers and noisy data. 
3 PROPOSED APPROACH 
In this section, we describe our approach which consists of two 
stages. The first stage focuses on the 3D cloud points preprocess 
ing. The second stage aims at the building footprint extraction. 
In this work, we assume that buildings have simple polygonal 
shapes. 
3.1 3D data preprocessing 
3.1.1 Partial Altering of redundant points As we have men 
tioned earlier, the laser sensor constantly sweeps the building fa 
cade even when the vehicle is stopped. Consequently, the ac 
quired raw data may contain many redundant frames due to this 
continuous acquisition. For this reason, we have defined a mea 
sure between two consecutive frames based on point-to-point dis 
tances. The redundant frames are thus detected and removed from