Full Papers 9:
Geometry III
Thursday, September 1st, 2005. 14:00 - 15:30
VENUE: Burke Theatre.
SESSION CHAIR: Alla Sheffer
Exploiting the Scanning Sequence for Automatic Registration of Large Sets of Range Maps
Paolo Pingi,
Andrea Fasano,
Paolo Cignoni,
Claudio Montani,
Roberto Scopigno,
Istituto di Scienza e Tecnologie dell'Informazione - Consiglio Nazionale delle Ricerche
Range map registration is still the most time consuming phase in the
processing of 3D scanning data. This is
because real scanning sets are composed of hundreds of range maps and
their registration is still partially manual.
We propose a new method to manage complex scan sets acquired by
following a regular scanner pose pattern. Our
goal is to define an initial adjacency graph by coarsely aligning
couples of range maps that we know are partially
overlapping thanks to the adopted scanning strategy. For a pair of
partially overlapping range maps, our iterative
solution locates pairs of correspondent vertices through the computation
of a regular n×n kernel which takes into
account vertex normals and is defined in the 2D space of the range map
(represented in implicit 2D format rather
than as a triangle mesh in 3D space). The shape-characterization kernel
and the metrics defined give a sufficiently
accurate shape matching, which has been proven to fit well the
requirements of automatic registration. This initial
set of adjacency arcs can then be augmented by the automatic
identification of the other significant arcs, by
adopting a criterion based on approximate range map overlap computation.
With respect to the solutions present
in literature, the simplifications and assumptions adopted make our
solution specifically oriented to complex 3D
scanning campaigns (hundreds of range maps). The proposed method can
coarsely register range maps in parallel
with the acquisition activity and this is a valuable help in assessing
on site the completeness of the sampling of
large objects.
Structure Preserving CAD Model Repair
Stephan Bischoff,
Leif Kobbelt,
RWTH Aachen, Lehrstuhl Informatik VIII
We present a technique which allows capture of 3D surface geometry
and a useful class of BRDFs using extremely simple equipment. A
standard digital camera with an attached flash serves as a portable
capture device, which may be used to sample geometry to very high
resolution, as well as supplying samples over a large portion of the
4D space on which the BRDF is defined. Importantly, it allows
capture of extended samples which may have spatially varying
(inhomogeneous) BRDF. We demonstrate the system by capturing the
geometry of complex materials with varying albedo and BRDF. We show
in-situ capture of materials such as a brick wall and a human hand.
The limitations of the system are that samples should be roughly
planar, and that the BRDF should have some diffuse component in
order that a first approximation to the normals can be computed.
However, given the simplicity and ease of use of the system (it
takes a few minutes to carefully capture a hand), and the ability to
capture extended surfaces without any range capture device such as a
laser scanner we argue that it is a valuable addition to the range
of real-world BRDF capture systems in the literature. We extend
standard photometric stereo techniques by moving both the camera and
the light source. By incorporating automatic parallax correction we
allow the capture of surfaces which are quite far from planar.
Cubical Marching Squares: Adaptive Feature Preserving Surface Extraction from Volume Data
Chien-Chang Ho,
Fu-Che Wu,
Bing-Yu Chen,
Yung-Yu Chuang,
Ming Ouhyoung,
National Taiwan University
In this paper, we present a new method for surface extraction from volume data which preserves sharp features,
maintains consistent topology and generates surface adaptively without crack patching. Our approach is based
on the marching cubes algorithm, a popular method to convert volumetric data to polygonal meshes. The original
marching cubes algorithm suffers from problems of topological inconsistency, cracks in adaptive resolution and
inability to preserve sharp features. Most of marching cubes variants only focus on one or some of these problems.
Although these techniques could be combined to solve these problems altogether, such a combination might not
be straightforward. Moreover, some feature-preserving variants introduce an additional problem, inter-cell dependency.
Our method provides a relatively simple and easy-to-implement solution to all these problems by converting
3D marching cubes into 2D cubical marching squares, resolving topology ambiguity with sharp features and eliminating
inter-cell dependency by sampling face sharp features. We compare our algorithm with other marching
cubes variants and demonstrate its effectiveness on various applications.
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