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Title
Close-range imaging, long-range vision

Jigital
di Milano,
).
one
pt GIS for
tional

AUTOMATIC REGISTRATION OF 3-D VIEWS
R. Bologna“, A. Guarnieri°, M. Minchilli", A. Vettore“
“ Dept. of Architecture and Town-Planning — Polytechnic of Bari (Italy)
? Dept. of Architecture and Planning — University of Sassari (Italy)
* CIRGEO (Interdept. Research Center of Cartography, Photogrammetry, Remote Sensing and GIS)
University of Padua — 35020 Legnaro (PD) — AGRIPOLIS - Italy
e-mail: cirgeo@unipd.it ; antonio.vettore@unipd.it
Commission V, WG V/4
KEY WORDS: 3D models, automatic registration, range data
ABSTRACT
Building of 3D models for single objects so as for whole environments, represents a continuosly and quickly evolving new research
field of the computer science. The arising interest in this field is strictly related with the increasing availability of more powerful
CPUs, overall in terms of PCs, which made possible, for the first time, to effectively manage complex 3D models off the research
centers, as well. A new set of issues has to be addressed in the 3D modeling of real objects. A lot of data are needed about the object
surface or volume, which have then to be aggregated, regardless the data format and the acquisition device used, in order to get the
final model. Actually, the data registration step requires a human operator, which were able to provide a first rough alignement
between acquired data. This approach is often time-consuming, increases the final cost of the 3D model and represents the major
limit to the wide spreading of real object models. Alternatively more sofisticated range data acquisition devices can be used, such a
range sensor mounted on a robotic arm with six degree of freedom, but anyway it is a very expensive modeling system.
In the light of topics previously exposed, a fully automatic range data registration system has been developed. This system is able to
execute all the steps needed for 3D modeling of real objects in automatic way or at least minimizing as more as possible the human
intervention, without any other information but the range data only.
In this paper, the subsystem for alignement of range data pairs is presented. The work draws the idea from A. E. Johnson[1], which
proposed an innovative solution for the recognition of similarities between 3D surfaces, introducing the spin-image concept. The
advantage of this approach rely on high computational robustness and effectiveness, which allows to employ standard market-level
CPUs. On the ground of the spin-image concept, a full data registration system was developed, in which the overlapping areas of two
adjacent data set are automatically recognized, thus allowing to correctly align the two whole data sets.
1. INTRODUCTION
Building of 3D models for single objects so as for whole
environments, represents a continuosly and quickly evolving
new research field of the computer science. The arising interest
in this field is strictly related with the increasing availability of
more powerful CPUs, overall in terms of PCs, which made
possible, for the first time, to effectively manage complex 3D
models off the research centers, as well. In the wide area of 3D
models we can distinguish two main categories: CAD or real
object models.
The models belonging to the first class are realized with a
workstation and are fully under the responsability of the
designer. These models can be created in a CAD environment to
build real objects, such for instance mechanical pieces, and
often they are then translated in the VRML format in order to be
distributed on the web. Sometimes an inverse approach is
needed: building a 3D model from a real object. Motivations at
the ground of this choice are different, i.e. such a models can be
used for reverse engineering or also in the medical field, to get
more qualitative and quantitative information than common 2D
images could offer. In this last case it should be considered the
3D modeling of anatomical parts such theeth or bones. Another
application of the second class of 3D models deals with the
need of filling an virtual environment with real objects, in order
to get a faithful copy of a real environment, such as the inside of
a museum or historical building.
Furthermore, 3D models of both abovementio-ned classes
represent an interesting tool for documentation and interactive
visualization purposes. For example they allow to represent 3D
objects more adequately than through single picture or
collection of pictures, and to change easily the user point of
view, providing in this way a useful tool by which the human
direct inspection can be well simulated.
A new set of issues has to be addressed in the 3D modeling of
real objects. A lot of data are needed about the object surface or
volume, which have then to be aggregated, regardless the data
format and the acquisition device used, in order to get the final
model. Actually, 3D modeling of real free-form surfaces consist
of the following steps:
1. Pairwise alignment of the 3D images;
2. Global alignment;
3. Fusion of the 3D data originally captured as clouds of points
into 3D surfaces;
4. Editing of possible surface holes due to minor missing data.
Step 2) and 3) are already performed automatically, while step
1) at the present requires a human operator, which were able to
provide a first rough alignement between acquired data. This
approach is often time-consuming, increases the final cost of the
3D model and represents the major limit to the wide spreading
of real object models.
Step 4), nowadays performed manually, may not be necessary if
an adequate amount of data is captured (which however may not
always be feasible).
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