961 research outputs found
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
Light Field Denoising via Anisotropic Parallax Analysis in a CNN Framework
Light field (LF) cameras provide perspective information of scenes by taking
directional measurements of the focusing light rays. The raw outputs are
usually dark with additive camera noise, which impedes subsequent processing
and applications. We propose a novel LF denoising framework based on
anisotropic parallax analysis (APA). Two convolutional neural networks are
jointly designed for the task: first, the structural parallax synthesis network
predicts the parallax details for the entire LF based on a set of anisotropic
parallax features. These novel features can efficiently capture the high
frequency perspective components of a LF from noisy observations. Second, the
view-dependent detail compensation network restores non-Lambertian variation to
each LF view by involving view-specific spatial energies. Extensive experiments
show that the proposed APA LF denoiser provides a much better denoising
performance than state-of-the-art methods in terms of visual quality and in
preservation of parallax details
A 4D Light-Field Dataset and CNN Architectures for Material Recognition
We introduce a new light-field dataset of materials, and take advantage of
the recent success of deep learning to perform material recognition on the 4D
light-field. Our dataset contains 12 material categories, each with 100 images
taken with a Lytro Illum, from which we extract about 30,000 patches in total.
To the best of our knowledge, this is the first mid-size dataset for
light-field images. Our main goal is to investigate whether the additional
information in a light-field (such as multiple sub-aperture views and
view-dependent reflectance effects) can aid material recognition. Since
recognition networks have not been trained on 4D images before, we propose and
compare several novel CNN architectures to train on light-field images. In our
experiments, the best performing CNN architecture achieves a 7% boost compared
with 2D image classification (70% to 77%). These results constitute important
baselines that can spur further research in the use of CNNs for light-field
applications. Upon publication, our dataset also enables other novel
applications of light-fields, including object detection, image segmentation
and view interpolation.Comment: European Conference on Computer Vision (ECCV) 201
Deep Depth From Focus
Depth from focus (DFF) is one of the classical ill-posed inverse problems in
computer vision. Most approaches recover the depth at each pixel based on the
focal setting which exhibits maximal sharpness. Yet, it is not obvious how to
reliably estimate the sharpness level, particularly in low-textured areas. In
this paper, we propose `Deep Depth From Focus (DDFF)' as the first end-to-end
learning approach to this problem. One of the main challenges we face is the
hunger for data of deep neural networks. In order to obtain a significant
amount of focal stacks with corresponding groundtruth depth, we propose to
leverage a light-field camera with a co-calibrated RGB-D sensor. This allows us
to digitally create focal stacks of varying sizes. Compared to existing
benchmarks our dataset is 25 times larger, enabling the use of machine learning
for this inverse problem. We compare our results with state-of-the-art DFF
methods and we also analyze the effect of several key deep architectural
components. These experiments show that our proposed method `DDFFNet' achieves
state-of-the-art performance in all scenes, reducing depth error by more than
75% compared to the classical DFF methods.Comment: accepted to Asian Conference on Computer Vision (ACCV) 201
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