RECONSTRUCTION OF 3 D VECTOR MODELS OF BUILDINGS BY COMBINATION OF ALS , TLS AND VLS DATA

Airborne Laser Scanning (ALS), Terrestrial Laser Scanning (TLS) and Vehicle based Laser Scanning (VLS) are widely used as data acquisition methods for 3D building modelling. ALS data is often used to generate, among others, roof models. TLS data has proven its effectiveness in the geometric reconstruction of building façades. Although the operating algorithms used in the processing chain of these two kinds of data are quite similar, their combination should be more investigated. This study explores the possibility of combining ALS and TLS data for simultaneously producing 3D building models from bird point of view and pedestrian point of view. The geometric accuracy of roofs and façades models is different due to the acquisition techniques. In order to take these differences into account, the surfaces composing roofs and façades are extracted with the same algorithm of segmentation. Nevertheless the segmentation algorithm must be adapted to the properties of the different point clouds. It is based on the RANSAC algorithm, but has been applied in a sequential way in order to extract all potential planar clusters from airborne and terrestrial datasets. Surfaces are fitted to planar clusters, allowing edge detection and reconstruction of vector polygons. Models resulting from TLS data are obviously more accurate than those generated from ALS data. Therefore, the geometry of the roofs is corrected and adapted according to the geometry of the corresponding façades. Finally, the effects of the differences between raw ALS and TLS data on the results of the modeling process are analyzed. It is shown that such combination could be used to produce reliable 3D building models.


INTRODUCTION
Airborne Laser Scanning (ALS), Terrestrial Laser Scanning (TLS), and Vehicle based Laser Scanning (VLS) are widely used as data acquisition methods for 3D building modelling.The main advantage of the laser scanning technique is that it allows to directly collect georeferenced sets of dense point clouds.Over the last decade, many significant developments in the field of laserscanning acquisition techniques have been performed.Also the laser data processing tools are constantly being improved (Shan and Toth, 2009).
ALS data are widely used in the production of Digital Terrain Models (DTM) and rough building models.The suitability of ALS techniques for 3D object reconstruction has been proven over the last decades.Nevertheless, the automatic generation of DTM as well as 3D building models remains an unresolved issue.This is essentially due to the weakness of information often given by the second echo compared to the first one.Actually, when this one is reliable, it helps to distinguish points belonging to the ground and non-ground surfaces (Tarsha-Kurdi et al., 2006).But in most cases, it is not always separable from the first one (Pfeifer et al., 1999).Currently, researchers rather use fullwaveform ALS data which provides the complete waveform of the backscattered signal produced by the illuminated object.Nevertheless, that kind of data is even rarely acquired and requires knowledge in signal processing techniques.
In all cases, ALS does not directly provide complete 3D models since they do not completely cover building facades.The acquisition angle and also the street narrowness make the façade acquisition difficult.In this case TLS and/or VLS are used.They provide high details point clouds at ground level since the access to the facade is facilitated.
In this context, a combination of ALS and TLS/VLS data for producing 3D building models simultaneously from a bird's point of view and pedestrian's point of view is suggested.This paper is structured as follows.Section 2 gives an overview related to LiDAR data processing.The test site and the used datasets are introduced in section3.Section 4 and 5 explain the processing chain developed for ALS and TLS data modeling respectively.The combination of both is discussed in section 6.

CONCLUSION
The point cloud acquired by an airborne LiDAR system and combined to VLS and TLS point clouds represent a model in itself, since the set of points provides a primary description of the buildings geometry.However, integration and management of these raw data in databases is problematic because of the huge amount of points.The 3D geometric modelling seems to be a good solution to this issue.Our approach enables the transformation of a model composed of points to a model composed of a small number of geometric shapes.In this form, the model is a starting point for other types of information, such as semantic and architectural information.
Properties of ALS, TLS and VLS data are different since they are collected in different ways.However, these differences can be exceeded by taking benefit from each one of them.TLS data must be used as geometric reference because the georeferencing and generally the inherent geometry of the clouds are more accurate than that of ALS data.Therefore the vector model of the roofs obtained from ALS data processing can be improved.ALS data is useful for a coarse modelling of façades which helps in the localization of individual façades and determining the hold of building.We can state that the airborne and ground LiDAR data are complementary if the georeferencing is controlled and corrected before processing.In that case, complete and reliable 3D models of buildings can be obtained.
In the future, more efforts will be achieved in the statistical study of combination of datasets coming from several laser scanning systems.The contribution of each processing steps in terms of processing time and disk space gain will also be studied.
Figure 7 p static and m Figure 10 shown in f At this sta those obta next sectio Figu and sThe way are e for T the r This section gives an overview of the main processing operations applied on ALS data as well as TLS and VLS data.Concerning the processing of ALS data, it starts with