21,845 research outputs found
Multi-view passive 3D face acquisition device
Approaches to acquisition of 3D facial data include laser scanners, structured
light devices and (passive) stereo vision. The laser scanner and structured light
methods allow accurate reconstruction of the 3D surface but strong light is projected
on the faces of subjects. Passive stereo vision based approaches do not require strong
light to be projected, however, it is hard to obtain comparable accuracy and robustness
of the surface reconstruction. In this paper a passive multiple view approach using
5 cameras in a ’+’ configuration is proposed that significantly increases robustness
and accuracy relative to traditional stereo vision approaches. The normalised cross
correlations of all 5 views are combined using direct projection of points instead of
the traditionally used rectified images. Also, errors caused by different perspective
deformation of the surface in the different views are reduced by using an iterative reconstruction
technique where the depth estimation of the previous iteration is used to
warp the windows of the normalised cross correlation for the different views
Efficient 3D data compression through parameterization of free-form surface patches
This paper presents a new method for 3D data compression based on parameterization of surface patches. The technique is applied to data that can be defined as single valued functions; this is the case for 3D patches obtained using standard 3D scanners. The method defines a number of mesh cutting planes and the intersection of planes on the mesh defines a set of sampling points. These points contain an explicit structure that allows us to define parametrically both x and y coordinates. The z values are interpolated using high degree polynomials and results show that compressions over 99% are achieved while preserving the quality of the mesh
Discrete Imaging Models for Three-Dimensional Optoacoustic Tomography using Radially Symmetric Expansion Functions
Optoacoustic tomography (OAT), also known as photoacoustic tomography, is an
emerging computed biomedical imaging modality that exploits optical contrast
and ultrasonic detection principles. Iterative image reconstruction algorithms
that are based on discrete imaging models are actively being developed for OAT
due to their ability to improve image quality by incorporating accurate models
of the imaging physics, instrument response, and measurement noise. In this
work, we investigate the use of discrete imaging models based on Kaiser-Bessel
window functions for iterative image reconstruction in OAT. A closed-form
expression for the pressure produced by a Kaiser-Bessel function is calculated,
which facilitates accurate computation of the system matrix.
Computer-simulation and experimental studies are employed to demonstrate the
potential advantages of Kaiser-Bessel function-based iterative image
reconstruction in OAT
Variational Autoencoders for Deforming 3D Mesh Models
3D geometric contents are becoming increasingly popular. In this paper, we
study the problem of analyzing deforming 3D meshes using deep neural networks.
Deforming 3D meshes are flexible to represent 3D animation sequences as well as
collections of objects of the same category, allowing diverse shapes with
large-scale non-linear deformations. We propose a novel framework which we call
mesh variational autoencoders (mesh VAE), to explore the probabilistic latent
space of 3D surfaces. The framework is easy to train, and requires very few
training examples. We also propose an extended model which allows flexibly
adjusting the significance of different latent variables by altering the prior
distribution. Extensive experiments demonstrate that our general framework is
able to learn a reasonable representation for a collection of deformable
shapes, and produce competitive results for a variety of applications,
including shape generation, shape interpolation, shape space embedding and
shape exploration, outperforming state-of-the-art methods.Comment: CVPR 201
3D Imaging of a Phase Object from a Single Sample Orientation Using an Optical Laser
Ankylography is a new 3D imaging technique, which, under certain
circumstances, enables reconstruction of a 3D object from a single sample
orientation. Here, we provide a matrix rank analysis to explain the principle
of ankylography. We then present an ankylography experiment on a microscale
phase object using an optical laser. Coherent diffraction patterns are acquired
from the phase object using a planar CCD detector and are projected onto a
spherical shell. The 3D structure of the object is directly reconstructed from
the spherical diffraction pattern. This work may potentially open the door to a
new method for 3D imaging of phase objects in the visible light region.
Finally, the extension of ankylography to more complicated and larger objects
is suggested.Comment: 22 pages 5 figure
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