Which side of the focal plane are you on?

Sellent, Anita; Favaro, Paolo

doi:10.1109/iccphot.2014.6831818

Cited by 20 publications

(21 citation statements)

References 23 publications

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“…Nevertheless, SIDFD suffers from two main limitations: first, there is an ambiguity related to the object's position in front or behind the in-focus plane; second, blur variation cannot be measured in the camera depth of field, leading to a dead zone. Ambiguity can be solved using asymmetrical coded aperture [14], or even by setting the focus at infinity, at a cost of reducing the light intensity that reaches the sensor or large depth of field (i.e., dead zone), respectively. Second, dead zones can be overcome using several images with various in-focus planes.…”

Section: Related Workmentioning

confidence: 99%

Deep Depth from Defocus: How Can Defocus Blur Improve 3D Estimation Using Dense Neural Networks?

Carvalho

Saux

Trouvé-Peloux

et al. 2019

Lecture Notes in Computer Science

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Depth estimation is of critical interest for scene understanding and accurate 3D reconstruction. Most recent approaches in depth estimation with deep learning exploit geometrical structures of standard sharp images to predict corresponding depth maps. However, cameras can also produce images with defocus blur depending on the depth of the objects and camera settings. Hence, these features may represent an important hint for learning to predict depth. In this paper, we propose a full system for single-image depth prediction in the wild using depth-fromdefocus and neural networks. We carry out thorough experiments to test deep convolutional networks on real and simulated defocused images using a realistic model of blur variation with respect to depth. We also investigate the influence of blur on depth prediction observing model uncertainty with a Bayesian neural network approach. From these studies, we show that out-of-focus blur greatly improves the depth-prediction network performances. Furthermore, we transfer the ability learned on a synthetic, indoor dataset to real, indoor and outdoor images. For this purpose, we present a new dataset containing real all-focus and defocused images from a Digital Single-Lens Reflex (DSLR) camera, paired with ground truth depth maps obtained with an active 3D sensor for indoor scenes. The proposed approach is successfully validated on both this new dataset and standard ones as NYUv2 or Depth-in-the-Wild. Code and new datasets are available at https:// github.com/ marcelampc/ d3net depth estimation.

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Section: Related Workmentioning

confidence: 99%

Deep Depth from Defocus: How Can Defocus Blur Improve 3D Estimation Using Dense Neural Networks?

Carvalho

Saux

Trouvé-Peloux

et al. 2019

Lecture Notes in Computer Science

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“…Varying the depth d, under a realistic scenario where x > f C , the blur radius is a convex function of d, with a minimum at the focal plane. This creates an ambiguity that was solved recently using coded-aperture lenses [1] or calibrated and memorized for modified lenses over shallow pre-defined depth ranges [2]. Here, we leverage the differential blur across spectral channels.…”

Section: Mathematical Framework and Solutionmentioning

confidence: 99%

Closed-Form Solution to Disambiguate Defocus Blur in Single-Perspective Images

Helou¹,

Shahpaski²,

Süsstrunk³

2019

Imaging and Applied Optics 2019 (COSI, IS, MATH, pcAOP)

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“…Sellent [17] resolved the focal plane ambiguity by means of using coded filter. Filters are coded such a way that the deblurring PSF is a function of the distance and the depth can be uniquely determined.…”

Section: Introductionmentioning

confidence: 99%

Resolving focal plane ambiguity in depth map creation from defocus blur using chromatic aberration

Kumar

Gupta

Venkatesh

2015

2015 10th International Conference on Information, Communications and Signal Processing (ICICS)

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Focal plane ambiguity in depth map creation from defocus blur has remained an challenging problem. In this paper, we present a method to resolve this issue with the help of Chromatic Aberration(CA). CA is a distortion referred to focal length variation of the lens with wavelength of light. When light, a mixture of various monochromatic components, passes through a lens, multiple focal planes are generated due to CA. There also exists an inherent ordering in the defocus blur of the object for different RGB components depending on whether the object lies in the near or far focus region. The ordering reverses as soon as focal planes are crossed. By using this difference in ordering of the defocus amount for different RGB components of an object, we can deduce whether the object is present in front of or behind the image plane, hence using this information, a more reliable depth map can be obtained.

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Which side of the focal plane are you on?

Cited by 20 publications

References 23 publications

Deep Depth from Defocus: How Can Defocus Blur Improve 3D Estimation Using Dense Neural Networks?

Deep Depth from Defocus: How Can Defocus Blur Improve 3D Estimation Using Dense Neural Networks?

Closed-Form Solution to Disambiguate Defocus Blur in Single-Perspective Images

Resolving focal plane ambiguity in depth map creation from defocus blur using chromatic aberration

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