SlideShare a Scribd company logo
Eyeglasses-free Display:
Towards Correcting Visual Aberrations with
Computational Light Field Displays
Fu-Chung Huang1,+ Gordon Wetzstein2,# Brian A. Barsky1 Ramesh Raskar2
University of California, Berkeley
MIT Media Lab
now at Microsoft
now at Stanford University
1
2
+
#
shown at 350mm
normal display
distance to display
focal
range
perceived image
normal display
distance to display
focal
range
pinhole array mask parallax barrier based light field display
Vision-correcting Displays @ SIGGRAPH 2014
Vision-correcting Displays @ SIGGRAPH 2014
Vision-correcting Displays @ SIGGRAPH 2014
Vision-correcting Displays @ SIGGRAPH 2014
Vision-correcting Displays @ SIGGRAPH 2014
Vision-correcting Displays @ SIGGRAPH 2014
25%
U.S. population of hyperopia (far-sightedness)
[Krachmer et al. 2005]
43%
age 40
U.S. population of presbyopia (need reading eyeglasses)
[Katz et al. 1997]
68%
age 80+
[Katz et al. 1997]
U.S. population of presbyopia (need reading eyeglasses)
43%
age 40
U.S. population of myopia (near-sightedness)
41.6%
[Vitale et al. 2009]
Myopia in some Asian countries
60% ~ 90%
[Rajan et al. 1995] [Wong et al. 2000]
[Takashima et al. 2001] [Lin et al. 2004]
Irregular Blurring in Vision
PSF PSF PSF
caused by higher-order aberrations
Nirmud lens (?)
9th century
reading stone
1284
Salvino D’Armato
1508
concept
1760
Benjamin Franklin
1880
August Mueller
1983
PRK and LASIK
now
934 B.C.
Computational light field display
(eye-tracking)
(input data)
Prior Work
Projector Precompensation
- Brown et al. [2006]
- Zhang and Nayer [2006]
- Oyamada et al. [2007]
- Grosse et al. [2010]
Computational Displays
- Lanman et al. [2010]
- Wetzstein et al. [2012]
- Maimone et al. [2013]
- Hirsch et al. [2014]
- Akeley et al. [2004]
Computational Vision Correction
- Alonso and Barreto [2003]
- Yellot and Yellot [2007]
- Huang et al. [2012]
- Pamplona et al. [2012]
- Ji et al. [2014]
- Huang and Barsky [2011]
How to Build a Vision Correcting Display
Spatial domain Frequency domain
=
⊗ ∗
=
𝑖𝑚𝑔 ∗ 𝑝𝑠𝑓 = 𝑏𝑙𝑢𝑟𝑖𝑚𝑔 ⊗ 𝑝𝑠𝑓 = 𝑏𝑙𝑢𝑟
Spatial domain Frequency domain
∗
=
−1
𝑖𝑚𝑔 ∗ 𝑝𝑠𝑓 = 𝑝𝑟𝑒
=
𝑖𝑚𝑔 ⊗ 𝑝𝑠𝑓 = 𝑝𝑟𝑒
⊗
−1
Spatial domain Frequency domain
prefiltered
perceived
=
𝑖𝑚𝑔 ⊗ 𝑝𝑠𝑓 = 𝑝𝑟𝑒
⊗
−1
Spatial domain
plane of focus time-multiplexed
PSFPSF
[Huang et al. 2012]
without correction multilayer displayconventional display
(simple inversion)
7x7 views into the eye
[Pamplona et al. 2012]
without correction
corrected vision
pupil aperture
Inversely Prefilter the Light Field
target image prefiltered light field
Flatland Light Field Projection
retina
𝐼(𝑥) =
−∞
+∞
𝑙 𝑥, 𝑢 𝐴 𝑢 𝑑𝑢Retinal image:
𝒙𝒖
𝒙
𝒖
display
focus plane
(1D image + 1D direction)
Light Field Projection
𝐼(𝑥) =
−∞
+∞
𝑙 𝑥, 𝑢 𝐴 𝑢 𝑑𝑢Retinal image:
𝒙
𝒖
retinadisplay
focus plane
𝒙𝒖
“Defocus” Light Field Projection
focus plane
𝒙
𝒖
retinadisplay
𝒙𝒖
“Defocus” Light Field Projection
focus plane
𝒙
𝒖
retinadisplay
𝒙𝒖
convolution
“Defocus” Light Field Projection
focus plane
𝒙
𝒖
retinadisplay
𝒙𝒖
convolution
𝝎 𝒙
𝝎 𝒖
frequency domain
analysis (in the paper)
Using a Light Field Display
𝒙
𝒖
𝒍 𝒅
=
−𝑟/2
𝑟/2
𝑙 𝑑 Ψ
𝑥
𝑢
𝑑𝑢
𝐼(𝑥) =
−∞
+∞
𝑙 𝑥, 𝑢 𝐴 𝑢 𝑑𝑢Retinal image:
more degrees
of freedom
focus plane
retina
𝒙𝒖
Using a Light Field Display
𝒍 𝒅
=
−𝑟/2
𝑟/2
𝑙 𝑑 Ψ
𝑥
𝑢
𝑑𝑢
𝐼(𝑥) =
−∞
+∞
𝑙 𝑥, 𝑢 𝐴 𝑢 𝑑𝑢Retinal image:
focus plane
retina
𝒙𝒖
?
Using a Light Field Display
𝒍 𝒅 𝒙𝒖
?
𝐏 ∙ 𝐋 𝒅 𝐈=
Using a Light Field Display
𝒍 𝒅 𝒙𝒖
?
𝐋 𝒅 𝐈= 𝐏−𝟏
𝐏 ∙
Using a Light Field Display
𝒙
𝒖
𝒍 𝒅
more degrees
of freedom
focus plane
retina
𝒙𝒖
𝐋 𝒅 𝐈= 𝐏−𝟏
Using a Light Field Display
𝒙
𝒖
𝒍 𝒅
more degrees
of freedom
focus plane
retina
𝒙𝒖
𝐋 𝒅 𝐈= 𝐏−𝟏
become well-posed?
Using a Light Field Display
𝒙
𝒖
𝒍 𝒅
more degrees
of freedom
focus plane
retina
𝒙𝒖
𝐋 𝒅 𝐈= 𝐏−𝟏
become well-posed?
Using a Light Field Display
𝒙
𝒖
𝒍 𝒅
more degrees
of freedom
focus plane
retina
𝒙𝒖
𝐋 𝒅 𝐈= 𝐏−𝟏
become well-posed?
Vision-correcting Displays @ SIGGRAPH 2014
Experiments and Results
250mm
focus380mm
f = 50 mm
a = 6 mm
Vision-correcting Displays @ SIGGRAPH 2014
Vision-correcting Displays @ SIGGRAPH 2014
without correction Pamplona et al. 2012multilayer prefilteringtarget image light field prefiltering
HDR-VDP2
Low error detection
Higher Order Aberrations
without
correction
conventional
display
lightfield
display
* =
:
:
:
:
Display light fieldProjection matrices
Axial or
lateral
movement
Vision-correcting Displays @ SIGGRAPH 2014
conventional
display
multilayer
display
[Pamplona et al.2012][Huang et al.2012] Proposed method
Method Inverse prefiltering Direct ray tracing Prefiltered light field
Spatial Resolution Very High Very Low High
Image Contrast Very Low Full (100%) High
Building Cost High Very High Very Low
light field
display
light field
display
Shortcomings
• Contrast and brightness loss
– Content-dependent
• Resolution loss
– 3-to-1(DroidDNA), 5-to-1(iPhone)
– about 150 PPI
• Computation
– GPU, Mobile
• Calibration
– Eye-tracking
– Off-Axis Opt.
Future Work
• Higher Resolution & Large Display
– e.g. tensor displays
• Multi-way correction
• Other applications
– AR/VR, 3D, Cryptography
• Theoretical analysis
– Higher order aberrations
Eyeglasses-free Display
http://web.media.mit.edu/~gordonw/VisionCorrectingDisplay/
http://graphics.berkeley.edu/papers/Huang-EFD-2014-08/
Fu-Chung Huang Gordon Wetzstein
Brian A. Barsky Ramesh Raskar
http://displayblocks.org/
Vision-correcting Displays @ SIGGRAPH 2014
Vision-correcting Displays @ SIGGRAPH 2014
Vision-correcting Displays @ SIGGRAPH 2014
Frequency Domain Analysis
𝝎 𝒙
𝝎 𝒖
(a) conventional display
(in-focus)
spatialdomainfrequencydomain
no angular variations
only spatial energy
𝒙
𝒖
𝝎 𝒙
𝝎 𝒖
𝒙
𝒖
(a) conventional display
(in-focus)
spatialdomainfrequencydomain
pupil function
is a rect(), in u
multiplication
( just spreading )
pupil response
is a sinc(), in 𝜔 𝑢
convolution
𝝎 𝒙
𝝎 𝒖
𝒙
𝒖
(a) conventional display
(in-focus)
spatialdomainfrequencydomain
retinal projection
integration in u
slicing at 𝜔 𝑢 = 0
𝐼 𝑥
𝐼 𝜔 𝑥
Fourier Slice Theorem
𝝎 𝒙
𝝎 𝒖
𝝎 𝒙
𝝎 𝒖
𝒙
𝒖
𝒙
𝒖
(a) conventional display
(in-focus)
(b) conventional display
(out-of-focus)
spatialdomainfrequencydomain
retinal projection
𝝎 𝒙
𝝎 𝒖
𝝎 𝒙
𝝎 𝒖
𝝎 𝒙
𝝎 𝒖
𝒙
𝒖
𝒙
𝒖
𝒙
𝒖
(a) conventional display
(in-focus)
(b) conventional display
(out-of-focus)
(c) multilayer display
(out-of-focus)
spatialdomainfrequencydomain
retinal projection
𝝎 𝒙
𝝎 𝒖
𝝎 𝒙
𝝎 𝒖
𝝎 𝒙
𝝎 𝒖
𝒙
𝒖
𝒙
𝒖
𝒙
𝒖
(a) conventional display
(in-focus)
(b) conventional display
(out-of-focus)
(c) multilayer display
(out-of-focus)
spatialdomainfrequencydomain
retinal projection
𝝎 𝒖
(d) light field display
(out-of-focus)
𝒙
𝒖
𝝎 𝒙
(d) light field display
(out-of-focus)
Vision-correcting Displays @ SIGGRAPH 2014

More Related Content

PPTX
Портфоліо бібліотекаря
PPTX
Compressive Light Field Projection @ SIGGRAPH 2014
PPTX
Tailored Displays to Compensate for Visual Aberrations - SIGGRAPH Presentation
PPTX
The Light Field Stereoscope | SIGGRAPH 2015
PPTX
SIGGRAPH 2012 Computational Plenoptic Imaging Course - 4 Light Fields
PPTX
SIGGRAPH 2012 Computational Plenoptic Imaging Course - 1 Introduction
PPTX
SIGGRAPH 2012 Computational Plenoptic Imaging Course - 7 Schlieren Imaging
PPTX
SIGGRAPH 2012 Computational Plenoptic Imaging Course - 2 High Dynamic Range I...
Портфоліо бібліотекаря
Compressive Light Field Projection @ SIGGRAPH 2014
Tailored Displays to Compensate for Visual Aberrations - SIGGRAPH Presentation
The Light Field Stereoscope | SIGGRAPH 2015
SIGGRAPH 2012 Computational Plenoptic Imaging Course - 4 Light Fields
SIGGRAPH 2012 Computational Plenoptic Imaging Course - 1 Introduction
SIGGRAPH 2012 Computational Plenoptic Imaging Course - 7 Schlieren Imaging
SIGGRAPH 2012 Computational Plenoptic Imaging Course - 2 High Dynamic Range I...

Viewers also liked (20)

PPTX
SIGGRAPH 2012 Computational Plenoptic Imaging Course - 3 Spectral Imaging
PPTX
Compressive Light Field Displays
PDF
Compressive DIsplays: SID Keynote by Ramesh Raskar
PPTX
SIGGRAPH 2012 Computational Display Course - 3 Computational Light Field Disp...
PPTX
Multi-camera Time-of-Flight Systems | SIGGRAPH 2016
PPT
CORNAR: Looking Around Corners using Trillion FPS Imaging
PPTX
VR2.0: Making Virtual Reality Better Than Reality?
PPTX
Light Field, Focus-tunable, and Monovision Near-eye Displays | SID 2016
PPTX
SIGGRAPH 2012 Computational Display Course - 1 introduction
PPTX
SIGGRAPH 2012 Computational Display Course - 2 Computational Displays
PPTX
Keynote - SPIE Stereoscopic Displays & Applications 2014
PPTX
>A Switchable Light Field Camera Architecture with Angle SEnsitive Pixels and...
PPTX
Optical Computing for Fast Light Transport Analysis
PDF
Eyeglasses-free Display: Towards Correcting Visual Aberrations with Computati...
PPTX
Adaptive Spectral Projection
PPTX
ProxImaL | SIGGRAPH 2016
PPTX
SIGGRAPH 2012 Computational Display Course - 4 Perceptually Driven Computatio...
PPT
VRay教學(一)-渲染器簡介
PDF
Introduction to Optical See-Through HMDs in AR
PPSX
5 Annoying Excel Pivot Table Problems
SIGGRAPH 2012 Computational Plenoptic Imaging Course - 3 Spectral Imaging
Compressive Light Field Displays
Compressive DIsplays: SID Keynote by Ramesh Raskar
SIGGRAPH 2012 Computational Display Course - 3 Computational Light Field Disp...
Multi-camera Time-of-Flight Systems | SIGGRAPH 2016
CORNAR: Looking Around Corners using Trillion FPS Imaging
VR2.0: Making Virtual Reality Better Than Reality?
Light Field, Focus-tunable, and Monovision Near-eye Displays | SID 2016
SIGGRAPH 2012 Computational Display Course - 1 introduction
SIGGRAPH 2012 Computational Display Course - 2 Computational Displays
Keynote - SPIE Stereoscopic Displays & Applications 2014
>A Switchable Light Field Camera Architecture with Angle SEnsitive Pixels and...
Optical Computing for Fast Light Transport Analysis
Eyeglasses-free Display: Towards Correcting Visual Aberrations with Computati...
Adaptive Spectral Projection
ProxImaL | SIGGRAPH 2016
SIGGRAPH 2012 Computational Display Course - 4 Perceptually Driven Computatio...
VRay教學(一)-渲染器簡介
Introduction to Optical See-Through HMDs in AR
5 Annoying Excel Pivot Table Problems
Ad

Similar to Vision-correcting Displays @ SIGGRAPH 2014 (20)

PDF
Sig2014 vision correcting display
PPT
vision correcting display
PPTX
Light field
PPT
Raskar Keynote at Stereoscopic Display Jan 2011
PPTX
Computational Near-eye Displays with Focus Cues - SID 2017 Seminar
PDF
#FTMA2017 鬼コース
PPT
MIT Camera Culture Group Update July 2009
PPTX
BYO3D 2011: Content
KEY
Light Field: New opportunities and applications
PPTX
SIGGRAPH 2014 Course on Computational Cameras and Displays (part 3)
PPTX
PPTX
Incident light field 1
PPTX
Incident light field 1
PPTX
BYO3D 2011: Emerging Technology
PDF
lfcamera-150dpi-Copier.pdf
PDF
Holus - Glassless 3d system and modular apparatus
PPTX
Emerging 3D Display Technologies
PPTX
Accommodation-invariant Computational Near-eye Displays - SIGGRAPH 2017
Sig2014 vision correcting display
vision correcting display
Light field
Raskar Keynote at Stereoscopic Display Jan 2011
Computational Near-eye Displays with Focus Cues - SID 2017 Seminar
#FTMA2017 鬼コース
MIT Camera Culture Group Update July 2009
BYO3D 2011: Content
Light Field: New opportunities and applications
SIGGRAPH 2014 Course on Computational Cameras and Displays (part 3)
Incident light field 1
Incident light field 1
BYO3D 2011: Emerging Technology
lfcamera-150dpi-Copier.pdf
Holus - Glassless 3d system and modular apparatus
Emerging 3D Display Technologies
Accommodation-invariant Computational Near-eye Displays - SIGGRAPH 2017
Ad

More from StanfordComputationalImaging (9)

PDF
Gaze-Contingent Ocular Parallax Rendering for Virtual Reality
PPTX
Autofocals: Evaluating Gaze-Contingent Eyeglasses for Presbyopes - Siggraph 2019
PPTX
Non-line-of-sight Imaging with Partial Occluders and Surface Normals | TOG 2019
PPTX
End-to-end Optimization of Cameras and Image Processing - SIGGRAPH 2018
PPTX
Build Your Own VR Display Course - SIGGRAPH 2017: Part 5
PPTX
Build Your Own VR Display Course - SIGGRAPH 2017: Part 4
PPTX
Build Your Own VR Display Course - SIGGRAPH 2017: Part 3
PPTX
Build Your Own VR Display Course - SIGGRAPH 2017: Part 2
PPTX
Build Your Own VR Display Course - SIGGRAPH 2017: Part 1
Gaze-Contingent Ocular Parallax Rendering for Virtual Reality
Autofocals: Evaluating Gaze-Contingent Eyeglasses for Presbyopes - Siggraph 2019
Non-line-of-sight Imaging with Partial Occluders and Surface Normals | TOG 2019
End-to-end Optimization of Cameras and Image Processing - SIGGRAPH 2018
Build Your Own VR Display Course - SIGGRAPH 2017: Part 5
Build Your Own VR Display Course - SIGGRAPH 2017: Part 4
Build Your Own VR Display Course - SIGGRAPH 2017: Part 3
Build Your Own VR Display Course - SIGGRAPH 2017: Part 2
Build Your Own VR Display Course - SIGGRAPH 2017: Part 1

Recently uploaded (20)

PDF
A comparative study of natural language inference in Swahili using monolingua...
PDF
Video forgery: An extensive analysis of inter-and intra-frame manipulation al...
PDF
Enhancing emotion recognition model for a student engagement use case through...
PPTX
Tartificialntelligence_presentation.pptx
PDF
Web App vs Mobile App What Should You Build First.pdf
PPTX
cloud_computing_Infrastucture_as_cloud_p
PDF
WOOl fibre morphology and structure.pdf for textiles
PDF
Accuracy of neural networks in brain wave diagnosis of schizophrenia
PPTX
Group 1 Presentation -Planning and Decision Making .pptx
PPTX
TLE Review Electricity (Electricity).pptx
PPTX
KOM of Painting work and Equipment Insulation REV00 update 25-dec.pptx
PPTX
Digital-Transformation-Roadmap-for-Companies.pptx
PDF
Unlocking AI with Model Context Protocol (MCP)
PDF
Mushroom cultivation and it's methods.pdf
PPTX
A Presentation on Artificial Intelligence
PDF
From MVP to Full-Scale Product A Startup’s Software Journey.pdf
PDF
Getting Started with Data Integration: FME Form 101
PPTX
Chapter 5: Probability Theory and Statistics
PDF
Zenith AI: Advanced Artificial Intelligence
PDF
ENT215_Completing-a-large-scale-migration-and-modernization-with-AWS.pdf
A comparative study of natural language inference in Swahili using monolingua...
Video forgery: An extensive analysis of inter-and intra-frame manipulation al...
Enhancing emotion recognition model for a student engagement use case through...
Tartificialntelligence_presentation.pptx
Web App vs Mobile App What Should You Build First.pdf
cloud_computing_Infrastucture_as_cloud_p
WOOl fibre morphology and structure.pdf for textiles
Accuracy of neural networks in brain wave diagnosis of schizophrenia
Group 1 Presentation -Planning and Decision Making .pptx
TLE Review Electricity (Electricity).pptx
KOM of Painting work and Equipment Insulation REV00 update 25-dec.pptx
Digital-Transformation-Roadmap-for-Companies.pptx
Unlocking AI with Model Context Protocol (MCP)
Mushroom cultivation and it's methods.pdf
A Presentation on Artificial Intelligence
From MVP to Full-Scale Product A Startup’s Software Journey.pdf
Getting Started with Data Integration: FME Form 101
Chapter 5: Probability Theory and Statistics
Zenith AI: Advanced Artificial Intelligence
ENT215_Completing-a-large-scale-migration-and-modernization-with-AWS.pdf

Vision-correcting Displays @ SIGGRAPH 2014

Editor's Notes

  • #3: Imagine you are near-sighted, When the display is shown outside your focal range,
  • #4: everything appears blurred
  • #5: We built a parallax barrier based light field display using a printed pinhole mask on a ipod touch And the construction is quite thin, just a few milimeters,
  • #6: And using computation to correct vision problem! This does not require the observer to wear eyeglasses. And we did not change the optics of the eye and display Once the display has been built, all the corrections are done through computation! So let me introduce where you can use this kind of technology…
  • #12: So, this is a technology that has a very broad impact….. But let me go more specific on who can benefit from this. It is estimated that about 25% of people in the US , are far-sighted
  • #13: And since the ability to accommodate will decreases over time, At age of 40, the population of people having presbyopia is about 43%,
  • #14: And the number increases to almost 70% at the age of 80, This is an inevitable aging process that everyone has to face. That we will need a pair of reading glasses
  • #15: In the mean time, recent study shows that myopia in the US has increased to 41% Which is also high
  • #16: But the number in some Asia countries is approaching to a crazy 60%~90%. Although these “conditions” can be solved with eyeglasses They are not always convenient (click)
  • #17: And there are also certain people having HOA, that the blur they perceive are irregular, and are very difficult to correct. So maybe we need a new solution, Before that, let me briefly review what options people have so far.
  • #18: The earliest form of correction is the reading stone, or just a magnifier. (click)But really the first revolution is the eyeglasses; (click) The second category is the contact lenses, but it took a long time to become what it is today. (click) Finally, the third category is refractive surgery, and it has become quite popular these days.
  • #19: In this paper, we introduce the 4th option, which is a computation based correction. (click) This eye-glasses free vision correcting display modifies the content shown on the display according to your prescription (click) When paired with standard eye tracking technology, the display can generate a sharp perception to the user’s eye. This capability is unlocked by solving a deconvolution problem in the light field domain. So let me first introduce some prior work that leads to this idea
  • #20: The idea of pre-correction is not new. Early work pre-compensate a defocused projector, such that the projected image will become sharp. (click) We are also inspired by the concept of computational display, that modifying the optical components and the added computation give us more degree of freedoms. (click) Finally, correcting vision through computation has existed for quite some time, but modern approaches using computational display can give better results.
  • #21: So let me briefly describe how to build a display that can potentially correct your vision
  • #22: Let’s consider a 1D scan line of the watch face, its diagram is shown on the right
  • #23: For we know the blur can be modeled using convolution, (click) In the frequency domain, the operator becomes a pairwise multiplication of their spatial frequencies
  • #24: A simple idea of prefiltering is to invert the kernel in frequency domain, multiply with the signal spectrum (click)And then transform it back to the spatial domain This is a very simple idea, and can be implemented in just 3 lines of code
  • #25: The result looks very strange. But the ringing artifacts will be canceled out by a final stage of the blur, which happens inside the eye. Unfortunately the perceived image has very low contrast. This is because inverting the kernel, or deconvolution, is a well-known ill-posed problem. So how can we solve the problem?
  • #26: Our prior work uses a multilayer display, (click)such that the point spread functions at different layers have different sizes In the frequency domain, they give different response, thus we have more degrees of freedom. (click) Here are the photograph comparing with conventional display. Multilayer display can generate higher contrast and sharper image. But the contrast is still not perfect, so let’s look at another approach.
  • #27: Pamplona et al. construct a high angular resolution light field display. (click)When shown to a patient with blurred vision, the display can generate a sharp perception. (click) In their solution, all the light rays hitting the same retinal cell will be assigned to the same value. (click) But since they requires all 49 views entering the eye, thus the hardware construction can be very challenging, and the spatial resolution is low. So there must be some way to have both high contrast and high resolution image.
  • #28: And actually it’s quite simple: We use a light field display, and inversely prefilter the 4D space, And you can see the prefiltered images inside each view have the amplified frequencies. So let me briefly describe how that is done.
  • #29: Let’s say you have a simple set up that the rays leaving the display are focused on the retina. To obtain the perceived image I, (click) we have to consider the retinal light field L, where the rays are expressed using the spatial location x and the angular direction u. (click) the retinal image pixel is obtained by integrating rays along the angular direction,
  • #30: And Finally, since some rays are blocked by the pupil, we have to multiply a binary aperture function A, That limits the integration. This gives us the basic projection equation
  • #31: Now let’s consider the case where the display is out of focus, (click) since the rays converge at a plane before the retina plane, the retinal light field is now sheared.
  • #32: Integrating along the angular direction changes the formulation into a convolution:
  • #33: There are some interesting frequency domain analysis, please refer to the paper. But basically we know that the inversion in the frequency domain is ill-posed, since we don’t have enough degrees of freedoms.
  • #34: So using a light field display allows us to control the angular variation, thus we have more flexibility. (click) so let’s go back to look again at the projection equation (click) Since we know the exact geometry, we can always express the integral using the display light field
  • #35: So the question is: how can we solve for the display light field such that the eye will see a sharp target image? Well, The first step is to discretize the integral equation
  • #36: Into a matrix-vector multiplication. (click) The projection matrix P can be obtained by sampling the light transport. And P times the light field gives the target image
  • #37: A very simple way to solve for the prefiltered display light field is to move the projection matrix to the right, And that’s it. The solution is almost just that simple. But we might ask ourselve is there any condition allowing us to solve this problem in such a simple way?
  • #38: Remember that we said using the light field display gives more degree of freedoms
  • #39: So does that mean we can solve it with just two rays per pixel?
  • #40: Or do we need 3 rays?
  • #41: Or maybe 4? To answer these question, (click) we can take a look at the property of the projection matrix P. Since we want to invert the matrix, it is a good idea to look at its condition number
  • #42: By changing the numbers of rays entering the pupil, and varying the blur size in screen pixels What we found is that the decrease of the condition number slows down (click) as the angular sampling rate is higher than 2, so we decide 2 rays per pixel is good enough to implement on a regular display This number gives the guideline for hardware construction,
  • #43: So let me show you the experiments and results(click)
  • #44: And we simulate a hyperopic eye using a camera. On the right is the direct comparison of a battery with our correction.
  • #45: And here is the video I showed you earlier.(click) Here we compare with the prior light field solution by pamplona et al. On the same low resolution hardware, we can still manage to create a sharp image.
  • #46: The hardware construction is quite straightforward, and we will show how to build it in a few seconds First we show a light field image, When the display is out of focused, everything is blurred. Putting a pinhole array mask on top, a sharp image is revealed after some alignment.
  • #47: We also evaluate the standard Michaelson contrast The multilayer prefiltering gives sharp correction, but contrast is very low. (click) On the other hand, Pamplona et al. has full contrast, but using the same low resolution prototype, their results are still quite blurry. (click) Our method gives a good balance between sharpness and contrast. (click) We also test with the perceptual metric HDR-VDP2, and our method has low probability of differences detection
  • #48: Finally, we also show that it is possible to correct higher order aberrations. You can see the spherical is quite different from the defocus term, and both are difficult to correct. (click)The coma is more difficult with a cone shape PSF. (click)Finally, not all higher order term are difficult; For example, trefoil has 3 legs, and preserves more high frequency information making the prefiltering easier.
  • #49: Our method can also account for slight deviation in the lateral and axial movement. This is done by stacking multiple projection matrices into the linear system and solving an over-constrained optimization.
  • #50: On the top right is the prefiltered light field with an over-constrained lateral movement. Since the light field display also has repetitive viewing zones, this allows the display to be viewed with two eyes separated by 65mms.
  • #51: Finally, to summarize the features and problems with different approaches, The first multilayer display deconvolve the optical blur of the eye, the resolution is higher, but the contrast is low. Building a multilayer display is non-trivial. (click)Pamplona et al require a super high resolution display, so its spatial resolution is quite low, even it provides the full image contrast. Build such high resolution display is extremely difficult and expensive. (click)The last technology prefilters the 4D light filed, so it has both higher resolution and higher contrast, and the cost is just a few dollars
  • #52: But of course, we also still have some problems. First, the prefiltering is content dependent: so if you have a lot of high frequencies, the contrast and brightness will be lower. (click) Second, our parallax barrier implementation will introduce some resolution loss, and the highest resolution we built is about 150PPI (click)processing light field requires a lot of computation, but we think it shouldn’t be too hard to port to GPU (click)Finally, the vision correcting require a good calibration between the eye and the display. But fortunately Amazon just announce a very cool firephone and SDK to do that, And in the paper, we also implemented an off-axis optimization to deal with the problem
  • #53: However, there are still some future work to be done. First, we anticipate higher resolution on a larger display to be built, and we believe this could be achieve using the tensor display architecture. (click)And it would be also interesting to provide a multi-way correction for a shared display. (click) extending the prefiltering to other applications like AR/VR, 3D content, or even cryptography can be very interesting (click) and finally, we believe a more thorough theoretical analysis is required for higher order aberrations.
  • #58: But even I just told you how to solve for the light field with a simple equation, The frequency analysis is actually more interesting than the equation.
  • #59: Let’s first look at the frequency domain light field, For a in-focus diffuse object, its spectrum is simply a line.
  • #60: But remember there is a pupil aperture blocking the light, In the spatial domain it’s a multiplication with the pupil function (click)And in the frequency domain it’s a “convolution” with the pupil response, which is a “sinc” function Since the original light field spectrum is only a line, (click)the convolution is just a spreading of the line vertically.
  • #61: The final step to obtain the image is the integration of light rays over the angular direction. If you are familiar with computational tomography, the integration is a slicing in the freq. domain. So the target image spectrum is the slicing on the axis.
  • #62: For an out of focus diffuse object, its representation is sheared, and so does its frequency spectrum. If you just slice the axis, there will be nothing left except for the DC term. But the spreading due to the pupil aperture will deliver some information to the slicing axis. Unfortunately not all spatial frequencies are preserved, since the spreading is due to a “sinc” function.
  • #63: In the multilayer prefiltering case, the missing frequencies are covered by the other layer, so you can still preserve all spatial frequencies
  • #64: Finally, the light field display is a bit different, You have a full convolution with the entire frequency spectrum, but you also have a lot more flexibility.