369 research outputs found
Depth Fields: Extending Light Field Techniques to Time-of-Flight Imaging
A variety of techniques such as light field, structured illumination, and
time-of-flight (TOF) are commonly used for depth acquisition in consumer
imaging, robotics and many other applications. Unfortunately, each technique
suffers from its individual limitations preventing robust depth sensing. In
this paper, we explore the strengths and weaknesses of combining light field
and time-of-flight imaging, particularly the feasibility of an on-chip
implementation as a single hybrid depth sensor. We refer to this combination as
depth field imaging. Depth fields combine light field advantages such as
synthetic aperture refocusing with TOF imaging advantages such as high depth
resolution and coded signal processing to resolve multipath interference. We
show applications including synthesizing virtual apertures for TOF imaging,
improved depth mapping through partial and scattering occluders, and single
frequency TOF phase unwrapping. Utilizing space, angle, and temporal coding,
depth fields can improve depth sensing in the wild and generate new insights
into the dimensions of light's plenoptic function.Comment: 9 pages, 8 figures, Accepted to 3DV 201
Single exposure 3D imaging of dusty plasma clusters
We have worked out the details of a single camera, single exposure method to
perform three-dimensional imaging of a finite particle cluster. The procedure
is based on the plenoptic imaging principle and utilizes a commercial Lytro
light field still camera. We demonstrate the capabilities of our technique on a
single layer particle cluster in a dusty plasma, where the camera is aligned
inclined at a small angle to the particle layer. The reconstruction of the
third coordinate (depth) is found to be accurate and even shadowing particles
can be identified.Comment: 6 pages, 7 figures. Submitted to Rev. Sci. Inst
Exploring plenoptic properties of correlation imaging with chaotic light
In a setup illuminated by chaotic light, we consider different schemes that
enable to perform imaging by measuring second-order intensity correlations. The
most relevant feature of the proposed protocols is the ability to perform
plenoptic imaging, namely to reconstruct the geometrical path of light
propagating in the system, by imaging both the object and the focusing element.
This property allows to encode, in a single data acquisition, both
multi-perspective images of the scene and light distribution in different
planes between the scene and the focusing element. We unveil the plenoptic
property of three different setups, explore their refocusing potentialities and
discuss their practical applications.Comment: 9 pages, 4 figure
3D Face Reconstruction from Light Field Images: A Model-free Approach
Reconstructing 3D facial geometry from a single RGB image has recently
instigated wide research interest. However, it is still an ill-posed problem
and most methods rely on prior models hence undermining the accuracy of the
recovered 3D faces. In this paper, we exploit the Epipolar Plane Images (EPI)
obtained from light field cameras and learn CNN models that recover horizontal
and vertical 3D facial curves from the respective horizontal and vertical EPIs.
Our 3D face reconstruction network (FaceLFnet) comprises a densely connected
architecture to learn accurate 3D facial curves from low resolution EPIs. To
train the proposed FaceLFnets from scratch, we synthesize photo-realistic light
field images from 3D facial scans. The curve by curve 3D face estimation
approach allows the networks to learn from only 14K images of 80 identities,
which still comprises over 11 Million EPIs/curves. The estimated facial curves
are merged into a single pointcloud to which a surface is fitted to get the
final 3D face. Our method is model-free, requires only a few training samples
to learn FaceLFnet and can reconstruct 3D faces with high accuracy from single
light field images under varying poses, expressions and lighting conditions.
Comparison on the BU-3DFE and BU-4DFE datasets show that our method reduces
reconstruction errors by over 20% compared to recent state of the art
Structured Light-Field Focusing 3D Density Measurements of A Supersonic Cone
This study describes three-dimensional (3D) quantitative visualization of density field in a supersonic flow around a cone spike. A measurement of the density gradient is conducted within a supersonic wind tunnel facility at the Propulsion and Energy Research Laboratory at the University of Central Florida utilizing Structured Light-Field Focusing Schlieren (SLLF). In conventional schlieren and Shadowgraph techniques, it is widely known that a complicated optical system is needed and yet visualizable area depends on an effective diameter of lenses and mirrors. Unlike these techniques, SLLF is yet one of the same family as schlieren photography, it is capable of non-intrusive turbulent flow measurement with relatively low cost and easy-to-setup instruments. In this technique, cross-sectional area in the flow field that is parallel to flows can be observed while other schlieren methods measure density gradients in line-of-sight, meaning that it measures integrated density distribution caused by discontinuous flow parameters. To reconstruct a 3D model of shock structure, two-dimensional (2D) images are pictured to process in MATLAB. The ultimate goal of this study is to introduce a novel technique of SLLF and quantitative 3D shock structures generated around a cone spike to reveal the interaction between free-stream flow and the high-pressure region
Light Field Blind Motion Deblurring
We study the problem of deblurring light fields of general 3D scenes captured
under 3D camera motion and present both theoretical and practical
contributions. By analyzing the motion-blurred light field in the primal and
Fourier domains, we develop intuition into the effects of camera motion on the
light field, show the advantages of capturing a 4D light field instead of a
conventional 2D image for motion deblurring, and derive simple methods of
motion deblurring in certain cases. We then present an algorithm to blindly
deblur light fields of general scenes without any estimation of scene geometry,
and demonstrate that we can recover both the sharp light field and the 3D
camera motion path of real and synthetically-blurred light fields.Comment: To be presented at CVPR 201
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