575 research outputs found
Variational Disparity Estimation Framework for Plenoptic Image
This paper presents a computational framework for accurately estimating the
disparity map of plenoptic images. The proposed framework is based on the
variational principle and provides intrinsic sub-pixel precision. The
light-field motion tensor introduced in the framework allows us to combine
advanced robust data terms as well as provides explicit treatments for
different color channels. A warping strategy is embedded in our framework for
tackling the large displacement problem. We also show that by applying a simple
regularization term and a guided median filtering, the accuracy of displacement
field at occluded area could be greatly enhanced. We demonstrate the excellent
performance of the proposed framework by intensive comparisons with the Lytro
software and contemporary approaches on both synthetic and real-world datasets
Baseline and triangulation geometry in a standard plenoptic camera
In this paper, we demonstrate light field triangulation to determine depth distances and baselines in a plenoptic camera. The advancement of micro lenses and image sensors enabled plenoptic cameras to capture a scene from different viewpoints with sufficient spatial resolution. While object distances can be inferred from disparities in a stereo viewpoint pair using triangulation, this concept remains ambiguous when applied in case of plenoptic cameras. We present a geometrical light field model allowing the triangulation to be applied to a plenoptic camera in order to predict object distances or to specify baselines as desired. It is shown that distance estimates from our novel method match those of real objects placed in front of the camera. Additional benchmark tests with an optical design software further validate the model’s accuracy with deviations of less than 0:33 % for several main lens types and focus settings. A variety of applications in the automotive and robotics field can benefit from this estimation model
On the Information Rates of the Plenoptic Function
The {\it plenoptic function} (Adelson and Bergen, 91) describes the visual
information available to an observer at any point in space and time. Samples of
the plenoptic function (POF) are seen in video and in general visual content,
and represent large amounts of information. In this paper we propose a
stochastic model to study the compression limits of the plenoptic function. In
the proposed framework, we isolate the two fundamental sources of information
in the POF: the one representing the camera motion and the other representing
the information complexity of the "reality" being acquired and transmitted. The
sources of information are combined, generating a stochastic process that we
study in detail. We first propose a model for ensembles of realities that do
not change over time. The proposed model is simple in that it enables us to
derive precise coding bounds in the information-theoretic sense that are sharp
in a number of cases of practical interest. For this simple case of static
realities and camera motion, our results indicate that coding practice is in
accordance with optimal coding from an information-theoretic standpoint. The
model is further extended to account for visual realities that change over
time. We derive bounds on the lossless and lossy information rates for this
dynamic reality model, stating conditions under which the bounds are tight.
Examples with synthetic sources suggest that in the presence of scene dynamics,
simple hybrid coding using motion/displacement estimation with DPCM performs
considerably suboptimally relative to the true rate-distortion bound.Comment: submitted to IEEE Transactions in Information Theor
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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