326 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
Depth Estimation Through a Generative Model of Light Field Synthesis
Light field photography captures rich structural information that may
facilitate a number of traditional image processing and computer vision tasks.
A crucial ingredient in such endeavors is accurate depth recovery. We present a
novel framework that allows the recovery of a high quality continuous depth map
from light field data. To this end we propose a generative model of a light
field that is fully parametrized by its corresponding depth map. The model
allows for the integration of powerful regularization techniques such as a
non-local means prior, facilitating accurate depth map estimation.Comment: German Conference on Pattern Recognition (GCPR) 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
Light field image processing : overview and research issues
Light field (LF) imaging first appeared in the computer graphics community with the goal of photorealistic 3D rendering [1]. Motivated by a variety of potential applications in various domains (e.g., computational photography, augmented reality, light field microscopy, medical imaging, 3D robotic, particle image velocimetry), imaging from real light fields has recently gained in popularity, both at the research and industrial level.peer-reviewe
Light field reconstruction from multi-view images
Kang Han studied recovering the 3D world from multi-view images. He proposed several algorithms to deal with occlusions in depth estimation and effective representations in view rendering. the proposed algorithms can be used for many innovative applications based on machine intelligence, such as autonomous driving and Metaverse
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