Raskar Next Billion Cameras Siggraph 2009
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http://raskar.scripts.mit.edu/~raskar/nextbillioncameras/ Siggraph 2009 Course with Alyosha Efros, Ramesh Raskar, Steve Seitz
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Raskar Next Billion Cameras Siggraph 2009
- 1. Camera Culture Ramesh Raskar Alyosha Efros Ramesh Raskar Steve Seitz Siggraph 2009 Curated Course Next Billion Cameras http://raskar.scripts.mit.edu / nextbillioncameras /
- 7. Where are the ‘camera’s?
- 8. Where are the ‘camera’s?
- 10. Camera Culture Ramesh Raskar Alyosha Efros Ramesh Raskar Steve Seitz Siggraph 2009 Course Next Billion Cameras http://raskar.info/photo/
- 11. Camera Culture Ramesh Raskar Alyosha Efros Ramesh Raskar Steve Seitz Siggraph 2009 Course Next 100 Billion Cameras http://raskar.info/photo/
- 13. Can you look around a corner ?
- 14. Can you decode a 5 micron feature from 3 meters away with an ordinary camera ?
- 15. Convert LCD into a big flat camera? Beyond Multi-touch
- 16. Pantheon
- 17. How do we move through a space?
- 18. What is ‘interesting’ here?
- 19. Record what you ‘feel’ not what you ‘see’
- 22. Camera Culture Ramesh Raskar Ramesh Raskar Camera Culture http://raskar.scripts.mit.edu / nextbillioncameras /
- 24. Crowdsourcing http://www.wired.com/wired/archive/14.06/crowds.html Object Recognition Fakes Template matching Amazon Mechanical Turk: Steve Fossett search ReCAPTCHA=OCR
- 26. Community Photo Collections U of Washington/Microsoft: Photosynth
- 27. Beyond Visible Spectrum Cedip RedShift
- 28. Trust in Images From Hany Farid
- 29. Trust in Images From Hany Farid LA Times March’03
- 30. Cameras in Developing Countries http://news.bbc.co.uk/2/hi/south_asia/7147796.stm Community news program run by village women
- 31. Vision thru tongue http://www.pbs.org/kcet/wiredscience/story/97-mixed_feelings.html Solutions for the Visually Challenged http://www.seeingwithsound.com/
- 33. Traditional Photography Lens Detector Pixels Image Mimics Human Eye for a Single Snapshot : Single View, Single Instant, Fixed Dynamic range and Depth of field for given Illumination in a Static world Courtesy: Shree Nayar
- 34. Computational Photography Computational Illumination Computational Camera Scene : 8D Ray Modulator Display Generalized Sensor Generalized Optics Processing 4D Ray Bender Upto 4D Ray Sampler Ray Reconstruction Generalized Optics Recreate 4D Lightfield Light Sources Modulators 4D Incident Lighting 4D Light Field
- 36. Goal and Experience Low Level Mid Level High Level Hyper realism Raw Angle, spectrum aware Non-visual Data, GPS Metadata Priors Comprehensive 8D reflectance field Digital Epsilon Coded Essence Computational Photography aims to make progress on both axis Camera Array HDR, FoV Focal stack Decomposition problems Depth Spectrum LightFields Human Stereo Vision Transient Imaging Virtual Object Insertion Relighting Augmented Human Experience Material editing from single photo Scene completion from photos Motion Magnification Phototourism
- 37. 2 nd International Conference on Computational Photography Papers due November 2, 2009 http://cameraculture.media.mit.edu/iccp10
- 41. Dynamic Range Goal: High Dynamic Range Short Exposure Long Exposure
- 45. Digital Refocusing using Light Field Camera 125 μ square-sided microlenses [Ng et al 2005]
- 47. Multi-flash Camera for Detecting Depth Edges
- 48. Depth Edges Left Top Right Bottom Depth Edges Canny Edges
- 50. Flutter Shutter Camera Raskar, Agrawal, Tumblin [Siggraph2006] LCD opacity switched in coded sequence
- 51. Traditional Coded Exposure Image of Static Object Deblurred Image Deblurred Image
- 53. "Fast Separation of Direct and Global Components of a Scene using High Frequency Illumination," S.K. Nayar, G. Krishnan, M. D. Grossberg, R. Raskar, ACM Trans. on Graphics (also Proc. of ACM SIGGRAPH), Jul, 2006.
- 56. Blind Camera Sascha Pohflepp, U of the Art, Berlin, 2006
- 58. Scene Completion Using Millions of Photographs Hays and Efros, Siggraph 2007
- 59. Community Photo Collections U of Washington/Microsoft: Photosynth
- 60. Can you look around a corner ?
- 61. Can you look around a corner ? Kirmani, Hutchinson, Davis, Raskar 2009 Accepted for ICCV’2009, Oct 2009 in Kyoto Impulse Response of a Scene
- 62. Femtosecond Laser as Light Source Pico-second detector array as Camera
- 63. Coded Aperture Camera The aperture of a 100 mm lens is modified Rest of the camera is unmodified Insert a coded mask with chosen binary pattern
- 64. In Focus Photo LED
- 65. Out of Focus Photo: Open Aperture
- 66. Out of Focus Photo: Coded Aperture
- 70. Bokode
- 71. Barcodes markers that assist machines in understanding the real world
- 72. Bokode: ankit mohan, grace woo, shinsaku hiura, quinn smithwick, ramesh raskar camera culture group, MIT media lab imperceptible visual tags for camera based interaction from a distance
- 73. Defocus blur of Bokode
- 74. Image greatly magnified. Simplified Ray Diagram
- 75. Our Prototypes
- 77. Converting LCD Screen = large Camera for 3D Interactive HCI and Video Conferencing Matthew Hirsch, Henry Holtzman Doug Lanman, Ramesh Raskar BiDi Screen *
- 78. Beyond Multi-touch: Mobile Laptops Mobile
- 79. Light Sensing Pixels in LCD Display with embedded optical sensors Sharp Microelectronics Optical Multi-touch Prototype
- 80. Design Overview Display with embedded optical sensors LCD , displaying mask Optical sensor array ~2.5 cm ~50 cm
- 82. Design Vision Object Collocated Capture and Display Bare Sensor Spatial Light Modulator
- 83. Touch + Hover using Depth Sensing LCD Sensor
- 84. Overview: Sensing Depth from Array of Virtual Cameras in LCD
- 87. Camera Culture Ramesh Raskar Alyosha Efros Ramesh Raskar Steve Seitz Siggraph 2009 Course Next Billion Cameras http://raskar.info/photo/
- 93. Fernald, Science [Sept 2006] Shadow Refractive Reflective Tools for Visual Computing
- 95. 2 nd International Conference on Computational Photography Papers due November 2, 2009 http://cameraculture.media.mit.edu/iccp10
Editor's Notes
- Since we are adapting LCD technology we can fit a BiDi screen into laptops and mobile devices.
- Introduce gist.
- 4 blocks : light, optics, sensors, processing, (display: light sensitive display)
- Inference and perception are important. Intent and goal of the photo is important. The same way camera put photorealistic art out of business, maybe this new artform will put the traditional camera out of business. Because we wont really care about a photo, merely a recording of light but a form that captures meaningful subset of the visual experience. Multiperspective photos. Photosynth is an example.
- Many think all this is for CP
- Stereo-pair is a simple example of coded photography. Many decomposition problems, direct/global, diffuse/specular,
- 1:15
- Comparisons
- SIGGRAPH 2008 Class: Computational Photography Debevec: Illumination as Computing / Scene & Performance Capture August 2008 Nayar et al. used high-frequency illumination patterns to quickly separate “direct” and “global” components. Basically, the global components stay the same as you phase-shift high-frequency illumination on the scene, while the direct components appear and disappear at different pixels. Taking the minimum value over a sequence of phase shifts yields the global component, multiplied by the fill ratio of the patterns; the maximum minus the minimum yields the direct component.
- Inference and perception are important. Intent and goal of the photo is important. The same way camera put photorealistic art out of business, maybe this new artform will put the traditional camera out of business. Because we wont really care about a photo, merely a recording of light but a form that captures meaningful subset of the visual experience. Multiperspective photos. Photosynth is an example.
- Maybe all the consumer photographer wants is a black box with big red button. No optics, sensors or flash. If I am standing the middle of times square and I need to take a photo. Do I really need a fancy camera?
- The camera can trawl on flickr and retrieve a photo that is roughly taken at the same position, at the same time of day. Maybe all the consumer wants is a blind camera.
- Reversibly encode all the information in this otherwise blurred photo
- The glint out of focus shows the unusual pattern.
- Since we are adapting LCD technology we can fit a BiDi screen into laptops and mobile devices.
- Recall that one of our inspirations was this new class of optical multi-touch device. At the top you can see a prototype that Sharp Microelectronics has published. These devices are basically arrays of naked phototransistors. Like a document scanner, they are able to capture a sharp image of objects in contact with the surface of the screen. But as objects move away from the screen, without any focusing optics, the images captured this device are blurred.
- Our observation is that by moving the sensor plane a small distance from the LCD in an optical multitouch device, we enable mask-based light-field capture. We use the LCD screen to display the desired masks, multiplexing between images displayed for the user and masks displayed to create a virtual camera array. I’ll explain more about the virtual camera array in a moment, but suffice to say that once we have measurements from the array we can extract depth.
- This device would of course support multi-touch on-screen interaction, but because it can measure the distance to objects in the scene a user’s hands can be tracked in a volume in front of the screen, without gloves or other fiducials.
- Thus the ideal BiDi screen consists of a normal LCD panel separated by a small distance from a bare sensor array. This format creates a single device that spatially collocates a display and capture surface.
- So here is a preview of our quantitative results. I’ll explain this in more detail later on, but you can see we’re able to accurately distinguish the depth of a set of resolution targets. We show above a portion of portion of views form our virtual cameras, a synthetically refocused image, and the depth map derived from it.
- CPUs and computers don’t mimic the human brain. And robots don’t mimic human activities. Should the hardware for visual computing which is cameras and capture devices, mimic the human eye? Even if we decide to use a successful biological vision system as basis, we have a range of choices. For single chambered to compounds eyes, shadow-based to refractive to reflective optics. So the goal of my group at Media Lab is to explore new designs and develop software algorithms that exploit these designs.