December 2, Wednesday 14:15, Room 303, Jacobs Building
Recent results in geometric modeling and point
processing
Lecturer : Andrei Sharf
Lecturer homepage : http://www.idav.ucdavis.edu/~asharf
Affiliation : Center of Visual Computing,
Shenzhen Institute of Advanced
Technology(SIAT), Chinese
Most 3D shapes are nowadays acquired using range scanning
devices.
Currently, scanners are capable of capturing complex shapes,
large
urban scenes and lately even motion. The initial
representation of
the shape consists of several properly transformed depth
images,
resulting in a point sampling of the surface. Typically,
scan data
consist of missing parts, noise in point coordinates and
orientation,
outliers and non-uniform sampled regions. Without prior
assumptions
and user interventions, the reconstruction problem is ill
posed;
an infinite number of surfaces pass through or near the data
points.
One of today's principal challenges is the development of
robust point
processing and reconstruction techniques that deal with the
inherent
inconsistencies in the acquired data set.
In my talk I will present recent advances in geometric
modeling,
processing and reconstruction of point data. I will describe
a
deformable model for watertight manifold reconstruction. The
model yields
a correct topology interpretation of the reconstructed shape
and allows
topology control to a certain extent. Next, I will present a
topology-aware interactive reconstruction technique.
Topological ambiguities
in the data are automatically detected and user interaction
is used to
consolidate topology reconstruction. Following, I will
present a space-time
technique for the reconstruction of moving and deforming
objects. The motion
of the object is described as an incompressible flow of
matter which
overcomes deficiencies in the acquired data such as
persistent occlusions,
errors and even entirely missing frames. Motivated by recent
advancements
in sparse signal reconstruction, I will present a
"lower-than-L2" minimization
scheme for sparse reconstruction. The sparsity
principle gives rise to a
novel global reconstruction paradigm for sharp point set
surfaces which is
robust to noise.