SIGGRAPH 2014 Course on Computational Cameras and Displays (part 2)

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Recent advances in both computational photography and displays have given rise to a new generation of computational devices. Computational cameras and displays provide a visual experience that goes beyond the capabilities of traditional systems by adding computational power to optics, lights, and sensors. These devices are breaking new ground in the consumer market, including lightfield cameras that redefine our understanding of pictures (Lytro), displays for visualizing 3D/4D content without special eyewear (Nintendo 3DS), motion-sensing devices that use light coded in space or time to detect motion and position (Kinect, Leap Motion), and a movement toward ubiquitous computing with wearable cameras and displays (Google Glass). This short (1.5 hour) course serves as an introduction to the key ideas and an overview of the latest work in computational cameras, displays, and light transport.

Education

Computational Light Field Imaging
Gordon Wetzstein
MIT / Stanford University
media.mit.edu/~gordonw
displayblocks.org

Next-generation Imaging Devices
computation optics & electronics human visual
system
interaction
optics (compressive) computationsensing
Computational &
Compressive Displays
Computational
Imaging

Resources
Conferences
• SIGGRAPH, CVPR, ICCV/ECCV, PROCAMS/CCD Workshop
• IEEE Int. Conference on Computational Photography (ICCP)
• OSA Computational Optical Sensing and Imaging (COSI)
Courses
• SIGGRAPH 2012 course “Computational Plenoptic Imaging”
• SIGGRAPH 2014 course “Time-of-Flight” (after this, R 118-120)
• classes on “Computational Photography” (e.g., Stanford & MIT)

2D position
2D direction
x
q
The Light Field

sensor
lenslet
f
Fixed trade-off between spatial and angular resolution!
Integral Imaging – Lippmann 1908

Scene from
Above
Lenslet Array
[Lippman 1908], [Adelson and Wang 1992], [Ng et al. 2005]
Integral Imaging
# Sensor Pixels X
#SensorPixelsY

Scene from
Above
# Sensor Pixels X / # Views X
#SensorPixelsY/#ViewsY
Integral Imaging: Spatio-Angular Resolution Tradeoff!Spatio-Angular Resolution Tradeoff!

Key Insight: Light Field is Redundant!Scene from
Above

Exploit Redundancy
Computationally
Sparse
Reconstruction
Overcomplete Dictionaries
Light Field Atoms
Sparse Representation Optimal Optical Setup
Mask-based
Light Field Coding
Compressive Light Field Photography
ACM SIGGRAPH 2013

Captured2DImage4DReconstruction
=
Mask-Coded Projection
4DLightField
Basis Pursuit Denoise:
Sparse Coefficients!
Compressive Light Field Photography
ACM SIGGRAPH 2013

Output
Switchable Light Field Photography
ICCP 2014

Optics Algorithms Sensors
Lytro.com Dappled Photography, Veeraraghavan, et al.
?An angle-sensitive CMOS imager for single-
sensor 3D photography, Wang et al.
Computational Photography

Phase
gratings
Two
Interleaved
Diodes
Single Pixel
Output 1 (D1)
Output 2 (D2)
Angle Sensitive Pixel

Angle (degrees)
Response Amplitude (V)
Single Pixel!
Operating Principle: Talbot Effect
Sum for instantaneous high-res 2D image!

Physical Layout Impulse Response
2D ASP Tile
Demosaic image for quick low-res light field!

Computational Microscopy
Nature Methods 2014Functional Brain Imaging
C. elegans

Computational Microscopy
Nature Methods 2014Functional Brain Imaging
Captured Light Fieldoptics design by Marc Levoy

maximum intensity projection of volume
350um x 350 um x 24 um at 50Hz
~70 neurons in head region
Nature Methods 2014

maximum intensity projection of volume
700um x 700 um x 200 um at 20Hz
~5,000 neurons
zebrafish larvae
Nature Methods 2014

Computational Imaging at SIGGRAPH 2014
• Emerging Technologies
• “Comp. Sensing & Display” Papers Session, Tue
3:45-5:15, Hall A
• “Displays” Papers Session, Tue 10:45-12:15, Hall A

Optical Computing for Fast Light Transport Analysis Matthew O'Toole and Kiriakos N. Kutulakos. ACM SIGGRAPH Asia, 2010. We present a general framework for analyzing the transport matrix of a real-world scene at full resolution, without capturing many photos. The key idea is to use projectors and cameras to directly acquire eigenvectors and the Krylov subspace of the unknown transport matrix. To do this, we implement Krylov subspace methods partially in optics, by treating the scene as a black box subroutine that enables optical computation of arbitrary matrix-vector products. We describe two methods—optical Arnoldi to acquire a low-rank approximation of the transport matrix for relighting; and optical GMRES to invert light transport. Our experiments suggest that good-quality relighting and transport inversion are possible from a few dozen low-dynamic range photos, even for scenes with complex shadows, caustics, and other challenging lighting effects.

Light Field Photography IntroductionCamera Culture Group, MIT Media Lab

Introduction to Light FieldsCamera Culture Group, MIT Media Lab

3D Shape and Indirect Appearance by Structured Light Transport

Matthew O'Toole

3D Shape and Indirect Appearance by Structured Light Transport Matthew O'Toole, John Mather, and Kiriakos N. Kutulakos. CVPR, 2014. Abstract: We consider the problem of deliberately manipulating the direct and indirect light flowing through a time-varying, fully-general scene in order to simplify its visual analysis. Our approach rests on a crucial link between stereo geometry and light transport: while direct light always obeys the epipolar geometry of a projector-camera pair, indirect light overwhelmingly does not. We show that it is possible to turn this observation into an imaging method that analyzes light transport in real time in the optical domain, prior to acquisition. This yields three key abilities that we demonstrate in an experimental camera prototype: (1) producing a live indirect-only video stream for any scene, regardless of geometric or photometric complexity; (2) capturing images that make existing structured-light shape recovery algorithms robust to indirect transport; and (3) turning them into one-shot methods for dynamic 3D shape capture.

Multi-camera Time-of-Flight Systems | SIGGRAPH 2016

StanfordComputationalImaging

Temporal Frequency Probing for 5D Transient Analysis of Global Light Transport Matthew O'Toole, Felix Heide, Lei Xiao, Matthias B. Hullin, Wolfgang Heidrich, and Kiriakos N. Kutulakos. ACM SIGGRAPH, 2014. Abstract: We analyze light propagation in an unknown scene using projectors and cameras that operate at transient timescales. In this new photography regime, the projector emits a spatio-temporal 3D signal and the camera receives a transformed version of it, determined by the set of all light transport paths through the scene and the time delays they induce. The underlying 3D-to-3D transformation encodes scene geometry and global transport in great detail, but individual transport components (e.g., direct reflections, inter-reflections, caustics, etc.) are coupled nontrivially in both space and time. To overcome this complexity, we observe that transient light transport is always separable in the temporal frequency domain. This makes it possible to analyze transient transport one temporal frequency at a time by trivially adapting techniques from conventional projector-to-camera transport. We use this idea in a prototype that offers three never-seen-before abilities: (1) acquiring time-of-flight depth images that are robust to general indirect transport, such as inter-reflections and caustics; (2) distinguishing between direct views of objects and their mirror reflection; and (3) using a photonic mixer device to capture sharp, evolving wavefronts of "light-in-flight".

Raskar Keynote at Stereoscopic Display Jan 2011

Camera Culture Group, MIT Media Lab

Computational Displays in 4D, 6D, 8D We have explored how light propagates from thin elements into a volume for viewing for both automultiscopic displays and holograms. In particular, devices that are typically connected with geometric optics, like parallax barriers, differ in treatment from those that obey physical optics, like holograms. However, the two concepts are often used to achieve the same effect of capturing or displaying a combination of spatial and angular information. Our work connects the two approaches under a general framework based in ray space, from which insights into applications and limitations of both parallax-based and holography-based systems are observed. Both parallax barrier systems and the practical holographic displays are limited in that they only provide horizontal parallax. Mathematically, this is equivalent to saying that they can always be expressed as a rank-1 matrix (i.e, a matrix in which all the columns are linearly related). Knowledge of this mathematical limitation has helped us to explore the space of possibilities and extend the capabilities of current display types. In particular, we have designed a display that uses two LCD panels, and an optimisation algorithm, to produce a content-adaptive automultiscopic display (SIGGRAPH Asia 2010). (Joint work with R Horstmeyer, Se Baek Oh, George Barbastathis, Doug Lanman, Matt Hirsch and Yunhee Kim) http://cameraculture.media.mit.edu In other work we have developed a 6D optical system that responds to changes in viewpoint as well as changes in surrounding light. Our lenticular array alignment allows us to achieve such a system as a passive setup, omitting the need for electrical components. Unlike traditional 2D flat displays, our 6D displays discretize the incident light field and modulate 2D patterns in order to produce super-realistic (2D) images. By casting light at variable intensities and angles onto our 6D displays, we can produce multiple images as well as store greater information capacity on a single 2D film (SIGGRAPH 2008). Ramesh Raskar joined the Media Lab from Mitsubishi Electric Research Laboratories in 2008 as head of the Lab’s Camera Culture research group. His research interests span the fields of computational photography, inverse problems in imaging and human-computer interaction. Recent inventions include transient imaging to look around a corner, next generation CAT-Scan machine, imperceptible markers for motion capture (Prakash), long distance barcodes (Bokode), touch+hover 3D interaction displays (BiDi screen), low-cost eye care devices (Netra) and new theoretical models to augment light fields (ALF) to represent wave phenomena. In 2004, Raskar received the TR100 Award from Technology Review, which recognizes top young innovators under the age of 35, and in 2003, the Global Indus Technovator Award, instituted at MIT to recognize the top 20 Indian technology innovators worldwide. In 2009, he was awarded a Sloan Research Fellowship. In 2010, he received the Darpa Young Faculty award. He holds over 40 US patents and has received four Mitsubishi Electric Invention Awards. He is currently co-authoring a book on Computational Photography. http://raskar.info

Light field

Ujjayanta Bhaumik

Introduction to Camera Challenges - Ramesh RaskarCamera Culture Group, MIT Media Lab

SIGGRAPH 2012 Computational Plenoptic Imaging Course - 4 Light Fields

Gordon Wetzstein

Light Field Technology

Jeffrey Funk

These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to analyze how Light Field Technology is becoming economic feasible for an increasing number of applications. Light Field Cameras record all of the light fields in a picture instead of just one light field. This capability enables users to change the focus of pictures after they have been taken and to more easily record 3D data. These features are becoming economically feasible improvements because of rapid improvements in camera chips and micro-lens arrays (an example of micro-electronic mechanical systems, MEMS). These features offer alternative ways to do 3D sensing for automated vehicles and augmented reality and can enable faster data collection with telescopes.

SIGGRAPH 2012 Computational Plenoptic Imaging Course - 3 Spectral Imaging

Gordon Wetzstein

Primal-Dual Coding to Probe Light Transport

Matthew O'Toole

Primal-Dual Coding to Probe Light Transport Matthew O'Toole, Ramesh Raskar, and Kiriakos N. Kutulakos. ACM SIGGRAPH, 2012. Abstract: We present primal-dual coding, a photography technique that enables direct fine-grain control over which light paths contribute to a photo. We achieve this by projecting a sequence of patterns onto the scene while the sensor is exposed to light. At the same time, a second sequence of patterns, derived from the first and applied in lockstep, modulates the light received at individual sensor pixels. We show that photography in this regime is equivalent to a matrix probing operation in which the elements of the scene's transport matrix are individually re-scaled and then mapped to the photo. This makes it possible to directly acquire photos in which specific light transport paths have been blocked, attenuated or enhanced. We show captured photos for several scenes with challenging light transport effects, including specular inter-reflections, caustics, diffuse inter-reflections and volumetric scattering. A key feature of primal-dual coding is that it operates almost exclusively in the optical domain: our results consist of directly-acquired, unprocessed RAW photos or differences between them.

CORNAR: Looking Around Corners using Trillion FPS Imaging

Camera Culture Group, MIT Media Lab

Non-line-of-sight Imaging with Partial Occluders and Surface Normals | TOG 2019

StanfordComputationalImaging

Imaging objects obscured by occluders is a significant challenge for many applications. A camera that could “see around corners” could help improve navigation and mapping capabilities of autonomous vehicles or make search and rescue missions more effective. Time-resolved single-photon imaging systems have recently been demonstrated to record optical information of a scene that can lead to an estimation of the shape and reflectance of objects hidden from the line of sight of a camera. However, existing non-line-of-sight (NLOS) reconstruction algorithms have been constrained in the types of light transport effects they model for the hidden scene parts. We introduce a factored NLOS light transport representation that accounts for partial occlusions and surface normals. Based on this model, we develop a factorization approach for inverse time-resolved light transport and demonstrate high-fidelity NLOS reconstructions for challenging scenes both in simulation and with an experimental NLOS imaging system.

Compressive DIsplays: SID Keynote by Ramesh RaskarCamera Culture Group, MIT Media Lab

>A Switchable Light Field Camera Architecture with Angle SEnsitive Pixels and...

Matt Hirsch - MIT Media Lab

We propose a flexible light field camera architecture that is at the convergence of optics, sensor electronics, and applied mathematics. Through the co-design of a sensor that comprises tailored, Angle Sensitive Pixels and advanced reconstruction algorithms, we show that—contrary to light field cameras today—our system can use the same measurements captured in a single sensor image to recover either a high-resolution 2D image, a low-resolution 4D light field using fast, linear processing, or a high-resolution light field using sparsity-constrained optimization.

Single photon 3D Imaging with Deep Sensor Fusion

David Lindell

End-to-end Optimization of Cameras and Image Processing - SIGGRAPH 2018

StanfordComputationalImaging

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Adaptive Spectral Projection

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Neural Scene Representation & Rendering: Introduction to Novel View Synthesis

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An overview over the neural scene representation and rendering framework and an introduction to novel view synthesis approaches. Slides made for the Eurographics, CVPR, and SIGGRAPH courses on neural rendering, connected to the state-of-the-art report on Neural Rendering at Eurographics 2020. Feel free to re-use the slides! I just ask that you keep some form of attribution, either at the beginning of your presentation, or in the slide footer.

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Keywords: Signal processing, Applied optics, Computer graphics and vision, Electronics, Art, and Online photo collections A computational camera attempts to digitally capture the essence of visual information by exploiting the synergistic combination of task-specific optics, illumination, sensors and processing. We will discuss and play with thermal cameras, multi-spectral cameras, high-speed, and 3D range-sensing cameras and camera arrays. We will learn about opportunities in scientific and medical imaging, mobile-phone based photography, camera for HCI and sensors mimicking animal eyes. We will learn about the complete camera pipeline. In several hands-on projects we will build several physical imaging prototypes and understand how each stage of the imaging process can be manipulated. We will learn about modern methods for capturing and sharing visual information. If novel cameras can be designed to sample light in radically new ways, then rich and useful forms of visual information may be recorded -- beyond those present in traditional protographs. Furthermore, if computational process can be made aware of these novel imaging models, them the scene can be analyzed in higher dimensions and novel aesthetic renderings of the visual information can be synthesized. In this couse we will study this emerging multi-disciplinary field -- one which is at the intersection of signal processing, applied optics, computer graphics and vision, electronics, art, and online sharing through social networks. We will examine whether such innovative camera-like sensors can overcome the tough problems in scene understanding and generate insightful awareness. In addition, we will develop new algorithms to exploit unusual optics, programmable wavelength control, and femto-second accurate photon counting to decompose the sensed values into perceptually critical elements.

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SIGGRAPH 2014 Course on Computational Cameras and Displays (part 2)

1. Computational Light Field Imaging Gordon Wetzstein MIT / Stanford University media.mit.edu/~gordonw displayblocks.org

2. Next-generation Imaging Devices computation optics & electronics human visual system interaction optics (compressive) computationsensing Computational & Compressive Displays Computational Imaging

3. Resources Conferences • SIGGRAPH, CVPR, ICCV/ECCV, PROCAMS/CCD Workshop • IEEE Int. Conference on Computational Photography (ICCP) • OSA Computational Optical Sensing and Imaging (COSI) Courses • SIGGRAPH 2012 course “Computational Plenoptic Imaging” • SIGGRAPH 2014 course “Time-of-Flight” (after this, R 118-120) • classes on “Computational Photography” (e.g., Stanford & MIT)

4. 2D position 2D direction x q The Light Field

5. sensor lenslet f Fixed trade-off between spatial and angular resolution! Integral Imaging – Lippmann 1908

7. Scene from Above Lenslet Array [Lippman 1908], [Adelson and Wang 1992], [Ng et al. 2005] Integral Imaging # Sensor Pixels X #SensorPixelsY

8. Scene from Above Integral Imaging

9. Scene from Above # Sensor Pixels X / # Views X #SensorPixelsY/#ViewsY Integral Imaging: Spatio-Angular Resolution Tradeoff!Spatio-Angular Resolution Tradeoff!

10. Key Insight: Light Field is Redundant!Scene from Above

11. Exploit Redundancy Computationally Sparse Reconstruction Overcomplete Dictionaries Light Field Atoms Sparse Representation Optimal Optical Setup Mask-based Light Field Coding Compressive Light Field Photography ACM SIGGRAPH 2013

12. Captured2DImage4DReconstruction = Mask-Coded Projection 4DLightField Basis Pursuit Denoise: Sparse Coefficients! Compressive Light Field Photography ACM SIGGRAPH 2013

13. Output Switchable Light Field Photography ICCP 2014

14. Optics Algorithms Sensors Lytro.com Dappled Photography, Veeraraghavan, et al. ?An angle-sensitive CMOS imager for single- sensor 3D photography, Wang et al. Computational Photography

15. Phase gratings Two Interleaved Diodes Single Pixel Output 1 (D1) Output 2 (D2) Angle Sensitive Pixel

16. Angle (degrees) Response Amplitude (V) Single Pixel! Operating Principle: Talbot Effect Sum for instantaneous high-res 2D image!

17. Physical Layout Impulse Response 2D ASP Tile Demosaic image for quick low-res light field!

18.

19. Computational Microscopy Nature Methods 2014Functional Brain Imaging C. elegans

20. Computational Microscopy Nature Methods 2014Functional Brain Imaging Captured Light Fieldoptics design by Marc Levoy

21. maximum intensity projection of volume 350um x 350 um x 24 um at 50Hz ~70 neurons in head region Nature Methods 2014

22. Nature Methods 2014

23. maximum intensity projection of volume 700um x 700 um x 200 um at 20Hz ~5,000 neurons zebrafish larvae Nature Methods 2014

24. Nature Methods 2014

25. Computational Imaging at SIGGRAPH 2014 • Emerging Technologies • “Comp. Sensing & Display” Papers Session, Tue 3:45-5:15, Hall A • “Displays” Papers Session, Tue 10:45-12:15, Hall A

SIGGRAPH 2014 Course on Computational Cameras and Displays (part 2)

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