terça-feira, 8 de janeiro de 2013

2009f - Scale-Space for Union of 3D Balls

Abstract—Shape discretization through union of weighted
points or balls appears as a common representation in different
fields of computer graphics and geometric modeling.
Among others, it has been very successful for implicit surface
reconstruction with radial basis functions, molecular atomic
models, fluid simulation from particle systems and deformation
tracking with particle filters. These representations are
commonly generated from real measurements or numerical
computations, which may require filtering and smoothing operations.
This work proposes a smoothing mechanism for union
of balls that tries to inherit from the scale-space properties
of bi-dimensional curvature motion: it avoids disconnecting
the shape, prevents self-intersection, regularly decreases the
area and convexifies the shape. The smoothing is computed
iteratively by moving each ball of the union according to a
combination of projected planar curvature motions. Experiments
exhibits nice properties of this scale-space.
Keywords-Union of Balls; Scale Spaces; Curvature Motion;



2009e - Geometry Super-Resolution by Example


Abstract—The acquisition of high-resolution 3D models still
requires delicate and time-consuming processes. In particular,
each detail of the object should be scanned separately, although
they may be similar. This can be simplified by copying a small
set of details at different places of the model, synthesizing
high geometric resolution from details exemplars, as introduced
in this paper for three different contexts : when the detail
exemplars are scanned separately at high resolution, when
they are synthesized or edited from other models, or when
they are obtained by accumulating repeated instances of the
detail in the low-resolution scan. The main challenge here is
to correctly register the high-resolution details with the low
resolution model. To address this issue, this work proposes
a careful resolution manipulation of 3D scans at each step
of an automatic registration pipeline, combined with a robust
selection of alignments. This results in a fully automatic process
for geometry super-resolution by example. Experiments on synthetic
and real data sets show applicability in different contexts,
including resolution increase, noise removal by example and
geometric texture insertion.



2009d - Support Vectors Learning for Vector Field Reconstruction Marcos


Abstract—Sampled vector fields generally appear as measurements
of real phenomena. They can be obtained by the
use of a Particle Image Velocimetry acquisition device, or
as the result of a physical simulation, such as a fluid flow
simulation, among many examples. This paper proposes to
formulate the unstructured vector field reconstruction and
approximation through Machine-Learning. The machine learns
from the samples a global vector field estimation function
that could be evaluated at arbitrary points from the whole
domain. Using an adaptation of the Support Vector Regression
method for multi-scale analysis, the proposed method provides
a global, analytical expression for the reconstructed vector field
through an efficient non-linear optimization. Experiments on
artificial and real data show a statistically robust behavior of
the proposed technique.
Keywords-Discrete Vector Field; Support Vector Machine;



2009c - Random Walks for Vector Field Denoising


Abstract—In recent years, several devices allow to directly
measure real vector fields, leading to a better understanding
of fundamental phenomena such as fluid simulation or brain
water movement. This turns vector field visualization and
analysis important tools for many applications in engineering
and in medicine. However, real data is generally corrupted
by noise, puzzling the understanding provided by those tools.
Those tools thus need a denoising step as preprocessing,
although usual denoising removes discontinuities, which are
fundamental for vector field analysis. This paper proposes a
novel method for vector field denoising based on random walks
which preserve those discontinuities. It works in a meshless
setting; it is fast, simple to implement, and shows a better
performance than the traditional gaussian denoising technique.
Keywords-Discrete Vector Field; Denoising; Random Walk;
Markov Chain;



2009b - Learning good views through intelligent galleries


Abstract. The definition of a good view of a 3D scene is highly subjective and strongly depends on both the scene
content and the 3D application. Usually, camera placement is performed directly by the user, and that task may
be laborious. Existing automatic virtual cameras guide the user by optimizing a single rule, e.g. maximizing the
visible silhouette or the projected area. However, the use of a static pre-defined rule may fail in respecting the
user’s subjective understanding of the scene. This work introduces intelligent design galleries, a learning approach
for subjective problems such as the camera placement. The interaction of the user with a design gallery teaches
a statistical learning machine. The trained machine can then imitate the user, either by pre-selecting good views
or by automatically placing the camera. The learning process relies on a Support Vector Machines for classifying
views from a collection of descriptors, ranging from 2D image quality to 3D features visibility. Experiments of the
automatic camera placement demonstrate that the proposed technique is efficient and handles scenes with occlusion
and high depth complexities. This work also includes user validations of the intelligent gallery interface.
Keywords: Learning. Camera Positioning. Virtual Camera. Intelligent Camera. Good View.


2009a - Arch generated shear bands in granular systems


Abstract. We propose an arch based model, on cubic and square lattices, to simulate the internal mobility of
grains, in a dense granular system under shear. In this model, the role of the arches in granular transport presents
a non-linear dependence on the local values of the stress components that can be modeled geometrically. This
non-linearity is very important since a linear dependence on the stress will make the models behave similarly to
viscous fluids, which will not reproduce highly interesting properties of the sheared systems such as shear bands. In
special, we study a modified Couette flow and find the appearance of shear-bands in accordance with the literature.
Keywords: Shear band. Granular system. Transport properties. Bi-based cuprates.



2008b - Approximations by smooth transitions in binary space partitions


result without global optimization. It combines the flexibility of Binary Space Partitions Trees with the statistical
robustness of Smooth Transition Regression Trees. The construction of the tree is straightforward and easily
controllable, using error-driven metrics or external constraints. Moreover, it leads to a concise representation.
Applications on synthetic and real data, both scalar and vector-valued demonstrated the effectiveness of this
approach.
Keywords: Binary Space Partitions. Smooth Transition Regression Trees. Classification and Regression Tree.
Vector Fields Approximation. Curve Reconstruction. Geometric Modeling.