216 research outputs found
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
Mirrored Light Field Video Camera Adapter
This paper proposes the design of a custom mirror-based light field camera
adapter that is cheap, simple in construction, and accessible. Mirrors of
different shape and orientation reflect the scene into an upwards-facing camera
to create an array of virtual cameras with overlapping field of view at
specified depths, and deliver video frame rate light fields. We describe the
design, construction, decoding and calibration processes of our mirror-based
light field camera adapter in preparation for an open-source release to benefit
the robotic vision community.Comment: tech report, v0.5, 15 pages, 6 figure
Light field super resolution through controlled micro-shifts of light field sensor
Light field cameras enable new capabilities, such as post-capture refocusing
and aperture control, through capturing directional and spatial distribution of
light rays in space. Micro-lens array based light field camera design is often
preferred due to its light transmission efficiency, cost-effectiveness and
compactness. One drawback of the micro-lens array based light field cameras is
low spatial resolution due to the fact that a single sensor is shared to
capture both spatial and angular information. To address the low spatial
resolution issue, we present a light field imaging approach, where multiple
light fields are captured and fused to improve the spatial resolution. For each
capture, the light field sensor is shifted by a pre-determined fraction of a
micro-lens size using an XY translation stage for optimal performance
Absolute depth using low-cost light field cameras
Digital cameras are increasingly used for measurement tasks within engineering scenarios, often being part of metrology platforms. Existing cameras are well equipped to provide 2D information about the fields of view (FOV) they observe, the objects within the FOV, and the accompanying environments. But for some applications these 2D results are not sufficient, specifically applications that require Z dimensional data (depth data) along with the X and Y dimensional data. New designs of camera systems have previously been developed by integrating multiple cameras to provide 3D data, ranging from 2 camera photogrammetry to multiple camera stereo systems.
Many earlier attempts to record 3D data on 2D sensors have been completed, and likewise many research groups around the world are currently working on camera technology but from different perspectives; computer vision, algorithm development, metrology, etc. Plenoptic or Lightfield camera technology was defined as a technique over 100 years ago but has remained dormant as a potential metrology instrument. Lightfield cameras utilize an additional Micro Lens Array (MLA) in front of the imaging sensor, to create multiple viewpoints of the same scene and allow encoding of depth information. A small number of companies have explored the potential of lightfield cameras, but in the majority, these have been aimed at domestic consumer photography, only ever recording scenes as relative scale greyscale images.
This research considers the potential for lightfield cameras to be used for world scene metrology applications, specifically to record absolute coordinate data. Specific interest has been paid to a range of low cost lightfield cameras to; understand the functional/behavioural characteristics of the optics, identify potential need for optical and/or algorithm development, define sensitivity, repeatability and accuracy characteristics and limiting thresholds of use, and allow quantified 3D absolute scale coordinate data to be extracted from the images.
The novel output of this work is; an analysis of lightfield camera system sensitivity leading to the definition of Active Zones (linear data generation good data) and In-active Zones (non-linear data generation poor data), development of bespoke calibration algorithms that remove radial/tangential distortion from the data captured using any MLA based camera, and, a light field camera independent algorithm that allows the delivery of 3D coordinate data in absolute units within a well-defined measurable range from a given camera
Microlens array grid estimation, light field decoding, and calibration
We quantitatively investigate multiple algorithms for microlens array grid
estimation for microlens array-based light field cameras. Explicitly taking
into account natural and mechanical vignetting effects, we propose a new method
for microlens array grid estimation that outperforms the ones previously
discussed in the literature. To quantify the performance of the algorithms, we
propose an evaluation pipeline utilizing application-specific ray-traced white
images with known microlens positions. Using a large dataset of synthesized
white images, we thoroughly compare the performance of the different estimation
algorithms. As an example, we apply our results to the decoding and calibration
of light fields taken with a Lytro Illum camera. We observe that decoding as
well as calibration benefit from a more accurate, vignetting-aware grid
estimation, especially in peripheral subapertures of the light field.Comment: \copyright 2020 IEEE. Personal use of this material is permitted.
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