In: Stilla U, Rottensteiner F, Paparoditis N (Eds) CMRT09. IAPRS, Vol. XXXVIII, Part 3A/V4 — Paris, France, 3-4 September, 2009
BUILDING FOOTPRINT DATABASE IMPROVEMENT FOR 3D RECONSTRUCTION: A
DIRECTION AWARE SPLIT AND MERGE APPROACH
Bruno Vallet and Marc Pierrot-Deseilligny and Didier Boldo
IGN - Laboratoire MATIS 2/4 avenue Pasteur - 94165 Saint-Mand Cedex, France
bruno.vallet@ign.fr - http://recherche.ign.fr/labos/matis
Commission III/3
KEY WORDS: Photogrammetry, 3D reconstruction, building footprint, split and merge, segmentation
ABSTRACT:
In the context of 3D reconstruction of wide urban areas, the use of building footprints has shown to be of great help to achieve both
robustness and precision. These footprints however often present inconsistencies with the data (more than one building in the footprint,
inner courts, superstructures...) This paper presents a fast and efficient algorithm to enhance the building footprint database in order to
make subsequent 3D reconstructions easier, more accurate and more robust. It is based on a segmentation energy that is minimized by
a split and merge approach. The algorithm is demonstrated on a wide urban area of one square kilometer.
(a) Orthophotography and footprint (b) Shaded DEM and vegetation
mask
Figure 1 : Input to our algorithm
1 INTRODUCTION
The production of 3D models of urban areas has received a lot
of attention from the scientific community in the last decade be
cause of the broad range of its applications and the increase in
both quality and quantity of data. In this setup, it becomes more
and more crucial to design flexible tools to help human operators
achieving efficient and accurate reconstruction of wide urban ar
eas.
1.1 Problem statement
The problem of urban reconstruction consists in finding a 3D
model (in general a polygonal surface) that is as coherent as pos
sible with the input data. In our case where the footprints of
the buildings are given, we can use the efficient and robust ap
proach proposed in (Durupt and Taillandier, 2006). However,
this approach relies heavily on the quality of the building foot
print database, and might fail if the building to be reconstructed
contains altimetric discontinuities that are not present in its foot
print. This often happens in practice, and especially when:
• Two (or more) adjacent buildings with different roof heights
share the same footprint.
• The real footprint of a building is only a portion of the foot
print in the database (gardens, inner courts,...)
• The building has some superstructures which sizes and heights
are not negligeable with respect to the expected precision of
the reconstruction. This problem becomes increasingly dif
ficult as reconstructions gain in precision, and has already
been tackled in the context of photogrammetry (Bredif et
al„ 2007) (Domaika and Bredif, 2008).
More difficult cases are often a combination of the three cited
above, and require a manual intervention to enable a further re
construction. In general, this intervention consists in subdividing
the footprint by cutting through all (or most of) the altimetric dis
continuities. In a production framework, where large areas need
to be extensively reconstructed, it appears that this building foot
print database enhancement step is one of the most time consum
ing. Hence, the problem that we tackle in this paper is that of
automatizing this enhancement as a required preprocessing step
to 3D reconstruction. More precisely, our problem is to segment a
polygonal footprint into a set of non-overlapping polygonal sub
footprints that cover it entirely, such that the interface between
the sub-footprints corresponds to altimetric discontinuities. This
is a problem of segmentation of vector data (building footprints
database) guided by raster data (photos, DEM,...)
1.2 Available data
The data available in our study mainly consisted of:
• A set of 10 centimeter resolution aerial images with a high
recovery ratio around 60% (intraband + interband) in order
to ensure that each ground point is seen in at least 4 images,
covering an area of one square kilometer. The images are in
RGBI (the infrared channel is used to obtain the vegetation
mask).
• A vectorized cadastral map giving building footprints for the
same area. It consists in a set of polygonal footprints given
by their ordered list of points in ground coordinates (Figure
1 (a), green).
From this initial data, existing algorithms can be run to extract:
