Depth from Projector^|^apos;s Defocus Based on Multiple Focus Pattern Projection

Masuyama, Hitoshi; Kawasaki, Hiroshi; Furukawa, Ryo

doi:10.2197/ipsjtcva.6.88

Cited by 12 publications

(10 citation statements)

References 10 publications

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“…Active DFD or DFF Active depth estimation methods using defocus blur have been proposed in the literature using either DFF [8][9][10][11] or SIDFD. [12][13][14][15] In this paper, we focus on SIDFD, which is more simple to conduct experimentally since it does not require to dynamically control optical parameters during acquisition. Note that most of the active DFD and DFF methods, except, 8, 9 use the defocus blur from the projector.…”

Section: Related Workmentioning

confidence: 99%

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Active chromatic depth from defocus for industrial inspection

Buat

Trouvé-Peloux

Champagnat

et al. 2020

Unconventional Optical Imaging II

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In this paper we propose a new concept for a compact 3D sensor dedicated to industrial inspection, combining chromatic Depth From Defocus (DFD) and structured illumination. Depth is estimated from a single image using local estimation of the defocus blur. As industrial objects usually show poor texture information, which is crucial for DFD, we rely on structured illumination. In contrast with state of the art approaches for active DFD, which project sparse patterns on the scene, our method exploits a dense textured pattern and provides dense depth maps of the scene. Besides, to overcome depth ambiguity and dead zone of DFD with a classical camera, we use an unconventional lens with chromatic aberration, providing spectrally varying defocus blur in the camera color channels. We provide comparisons of depth estimation performance for several projected patterns at various scales based on simulation and real experiments. The proposed method is then qualitatively evaluated on a real industrial object. Finally we discuss the perspectives of this work especially in terms of co-design of an 3D active sensor using DFD.

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Section: Related Workmentioning

confidence: 99%

“…The main question in active DFD or DFF is the choice of the projected pattern. Most of methods use evenly spaced vertical lines 9,11,12,14 or evenly spaced dots. 10,13,15 Blur is then estimated by measuring the spread or the brightness of each elements of the pattern in the image.…”

Section: Choice Of Projected Patternmentioning

confidence: 99%

Active chromatic depth from defocus for industrial inspection

Buat

Trouvé-Peloux

Champagnat

et al. 2020

Unconventional Optical Imaging II

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“…Recently, several techniques that use multiple depth cues for active stereo systems have been proposed. A method devised by Masuyama et al relies on both stereo and DfD cues by projecting multiple patterns along the same optical axis but using different focal lengths [5]. However, sharing the same optical axis complicates the system, and overlapping multiple patterns severely lowers the contrast.…”

Section: Related Workmentioning

confidence: 99%

Learning-based active 3D measurement technique using light field created by video projectors

Shiba

Ono

Furukawa

et al. 2019

IPSJ T Comput Vis Appl

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The combination of a pattern projector and a camera is widely used for 3D measurement. To recover shape from a captured image, various kinds of depth cues are extracted from projected patterns in the image, such as disparities from active stereo or blurriness for depth from defocus. Recently, several techniques have been proposed to improve 3D quality using multiple depth cues by installing coded apertures in projectors or by increasing the number of projectors. However, superposition of projected patterns forms a complicated light field in 3D space, which makes the process of analyzing captured images challenging. In this paper, we propose a learning-based technique to extract depth information from such a light field, which includes multiple depth cues. In the learning phase, prior to the 3D measurement of unknown scenes, projected patterns as they appear at various depths are prepared from not only actual images but also ones generated virtually using computer graphics and geometric calibration results. Then, we use principal component analysis (PCA) to extract features of small patches. In the 3D measurement (reconstruction) phase, the same features of patches are extracted from a captured image of a target scene and compared with the learned data. By using the dimensional reduction by feature extraction, an efficient search algorithm, such as an approximated nearest neighbor (ANN), can be used for the matching process. Another important advantage of our learning-based approach is that we can use most known projection patterns without changing the algorithm.

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“…(9), G(u, v) is the FT of g(x, y), H(u, v) is the FT of h(x, y), F(u, v) is the FT of f(x, y) and u, v are the spatial frequencies in the x, y directions, respectively. The units u of and v are m -1 .…”

Section: Dfd Algorithm Using Focal Length As the Varying Parameter Fomentioning

confidence: 99%

“…Hardware solutions involve using multiple projected patterns each having a different axially located focus point to extend the working range of in-focus patterns [9]. But these units have increased hardware complexity without significant gain since only a few axial depth regions will have sharp focus retaining patterns.…”

Section: Introductionmentioning

confidence: 99%

Motionless active depth from defocus system using smart optics for camera autofocus applications

Amin

Riza

2016

SPIE Proceedings

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This paper describes a motionless active Depth from Defocus (DFD) system design suited for long working range camera autofocus applications. The design consists of an active illumination module that projects a scene illuminating coherent conditioned optical radiation pattern which maintains its sharpness over multiple axial distances allowing an increased DFD working distance range. The imager module of the system responsible for the actual DFD operation deploys an electronically controlled variable focus lens (ECVFL) as a smart optic to enable a motionless imager design capable of effective DFD operation. An experimental demonstration is conducted in the laboratory which compares the effectiveness of the coherent conditioned radiation module versus a conventional incoherent active light source, and demonstrates the applicability of the presented motionless DFD imager design. The fast response and no-moving-parts features of the DFD imager design are especially suited for camera scenarios where mechanical motion of lenses to achieve autofocus action is challenging, for example, in the tiny camera housings in smartphones and tablets. Applications for the proposed system include autofocus in modern day digital cameras.

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Depth from Projector^|^apos;s Defocus Based on Multiple Focus Pattern Projection

Cited by 12 publications

References 10 publications

Active chromatic depth from defocus for industrial inspection

Active chromatic depth from defocus for industrial inspection

Learning-based active 3D measurement technique using light field created by video projectors

Motionless active depth from defocus system using smart optics for camera autofocus applications

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