Wavefront and distance measurement using the CAFADIS camera

Rodríguez-Ramos, José Manuel; Castellá, Bruno Femenía; Nava, Fernando Pérez; Fumero, S.

doi:10.1117/12.789380

Cited by 19 publications

(7 citation statements)

References 11 publications

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“…22 in the context of applying them to a real-time 3D camera. 28 We expect to be able to estimate the depth map of a scene, with a more robust handling of the occlusions, from such a focal measure than what is possible with a stereo configuration. That objective has been partially adressed, and is discussed in Ref.…”

Section: Future Workmentioning

confidence: 99%

Fast approximate 4D:3D discrete Radon transform, from light field to focal stack with O(N⁴) sums

et al. 2011

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In this work we develop a new algorithm, that extends the bidimensional Fast Digital Radon transform from Götz and Druckmüller (1996), to digitally simulate the refocusing of a 4D light field into a 3D volume of photographic planes, as previously done by Ren Ng et al. (2005), but with the minimum number of operations. This new algorithm does not require multiplications, just sums, and its computational complexity is O(N 4 ) to achieve a volume consisting of 2N photographic planes focused at different depths, from a N 4 plenoptic image. This reduced complexity allows for the acquisition and processing of a plenoptic sequence with the purpose of estimating 3D shape at video rate. Examples are given of implementations on GPU and CPU platforms. Finally, a modified version of the algorithm to deal with domains of sizes different than power of two, is proposed.

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Section: Future Workmentioning

confidence: 99%

Fast approximate 4D:3D discrete Radon transform, from light field to focal stack with O(N⁴) sums

et al. 2011

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“…For depth extraction the plenoptic sensor acts as a multiview stereo system, whereas, for wavefront phase extraction, it can be understood as a global sensor containing as extreme cases the Shack--Hartmann sensor and the pyramid sensor (2 2 microlens array at focus). The tomographic capability of the plenoptic sensor was demonstrated by Rodríguez-Ramos et al [4], [5].…”

Section: Introductionmentioning

confidence: 97%

Design and Laboratory Results of a Plenoptic Objective: From 2D to 3D With a Standard Camera

Montilla

Puga

Luke

et al. 2015

J. Display Technol.

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The plenoptic camera was originally created to allow the capture of the light field, a four-variable volume representation of all rays and their directions, which allows the creation by synthesis of an image of the observed object. This method has several advantages with regard to 3D capture systems based on stereo cameras since it does not need frame synchronization or geometric and color calibration. It also has many applications, from 3DTV to medical imaging. A plenoptic camera uses a microlens array to measure the radiance and direction of all the light rays in a scene. The array is placed at a distance from the principal lens, which is conjugated to the distance where the scene is situated, and the sensor is at the focal plane of the microlenses. We have designed a plenoptic objective that incorporates a microlens array and a relay system that reimages the microlens plane. This novel approach has proven successful. Placing it on a camera, the plenoptic objective creates a virtual microlens plane in front of the camera CCD, allowing it to capture the light field of the scene. In this paper we present the experimental results showing that depth information is perfectly captured when using an external plenoptic objective. Using this objective transforms any camera into a 3D sensor, opening up a wide range of applications from microscopy to astronomy.

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“…Wavefront sensing: The purpose of this approach is to extract an estimation of the wavefront phase in the scene. This technique has been widely explored in astrophyshics to perform adaptive optics in telescopes [50,51,52,53,54]. Tracking and pose estimation: As the light field encodes 3D information about the scene, it can be used in visual odometry and navigation.…”

Section: Introductionmentioning

confidence: 99%

Implementation of a Depth from Light Field Algorithm on FPGA

Conde

Luke

Rosa

2019

Sensors

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A light field is a four-dimensional function that grabs the intensity of light rays traversing an empty space at each point. The light field can be captured using devices designed specifically for this purpose and it allows one to extract depth information about the scene. Most light-field algorithms require a huge amount of processing power. Fortunately, in recent years, parallel hardware has evolved and enables such volumes of data to be processed. Field programmable gate arrays are one such option. In this paper, we propose two hardware designs that share a common construction block to compute a disparity map from light-field data. The first design employs serial data input into the hardware, while the second employs view parallel input. These designs focus on performing calculations during data read-in and producing results only a few clock cycles after read-in. Several experiments were conducted. First, the influence of using fixed-point arithmetic on accuracy was tested using synthetic light-field data. Also tests on actual light field data were performed. The performance was compared to that of a CPU, as well as an embedded processor. Our designs showed similar performance to the former and outperformed the latter. For further comparison, we also discuss the performance difference between our designs and other designs described in the literature.

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Wavefront and distance measurement using the CAFADIS camera

Cited by 19 publications

References 11 publications

Fast approximate 4D:3D discrete Radon transform, from light field to focal stack with O(N⁴) sums

Fast approximate 4D:3D discrete Radon transform, from light field to focal stack with O(N⁴) sums

Design and Laboratory Results of a Plenoptic Objective: From 2D to 3D With a Standard Camera

Implementation of a Depth from Light Field Algorithm on FPGA

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Wavefront and distance measurement using the CAFADIS camera

Cited by 19 publications

References 11 publications

Fast approximate 4D:3D discrete Radon transform, from light field to focal stack with O(N4) sums

Fast approximate 4D:3D discrete Radon transform, from light field to focal stack with O(N4) sums

Design and Laboratory Results of a Plenoptic Objective: From 2D to 3D With a Standard Camera

Implementation of a Depth from Light Field Algorithm on FPGA

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Product

Resources

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Fast approximate 4D:3D discrete Radon transform, from light field to focal stack with O(N⁴) sums

Fast approximate 4D:3D discrete Radon transform, from light field to focal stack with O(N⁴) sums