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Lost Decade of Computational Photography

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Camera Culture Group, MIT Media Lab
Camera Culture Group, MIT Media Lab

I recently gave a talk at ICCP 2015 and clarified that we should stop working on coded aperture for focus effects! (Thus negating my team's work in this area.). I also spoke about the lost decade of computational photography and how we have wasted too many years working on the wrong problems.

Technology
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Camera Culture Ramesh Raskar Ramesh Raskar Asso. Professor MIT Media Lab Computational Photography Lost Decade .. Glorious Ahead?
Friend: Past: Now: Future: Immature Mature Expectations Delights Drag: Tricks Crutch:
SLR-like Friend: Past: Now: Future: Immature Mature Expectations Delights Drag: Super Human Vision Crutch:
Camera Phone SLR-like Super Human Vision Friend: Pixel Hacking Past: Now: Future: Immature Mature Expectations Burst Mode Tricks Epsilon Photography Delights Drag: Lost Decade of Computational Photography
Goal and Experience Low Level Mid Level High Level Hyper realism Raw Angle, spectrum aware Non-visual Data, GPS Metadata Priors Capture Process Comprehensive 8D reflectance field Digital Epsilon Coded Essence CP aims to make progress on both axis Camera Array HDR, FoV Focal stack Decompositio n 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
Future of Imaging • What will a camera/display look like in 20 years? • How will the next billion cameras change the social culture? • How can we augment the camera to support best ‘image search’? • How will portable health diagnostics impact healthcare? • Will we live mostly in virtual/augmented reality, telepresent? TiVo for life? • How will ultra-high-speed/resolution imaging change us? • How can we improve ‘trust’ in imaging? • Can we print anything .. Cars, food, .. babies .. (Amazon?) • What are the opportunities in pervasive recording? e.g. GoogleEarth Live • What will be in Photoshop2030? • What is the future of movie-making, news reporting, sports viewing?
Camera of the Future Cloth Blimp/Drone Flat-cam Retinal Implant Disappear into gadgets
Displays Glasses free 3D Lighting Sensitive Holograms Touch + 3D Hover High Frame Rate Multi-Color Glasses free 2D
Can you see around corners ?
2nd Bounce Multi-path Analysis 1st Bounce 3rd Bounce 11
1 2 Optical Brush
Heshmat, Lee, Raskar,, Nature Scientific Reports, 2016 Optical Brush
Camera Phone SLR-like Super Human Vision Friend: Pixel Hacking Past: Now: Future: Immature Mature Expectations Burst Mode Tricks Delights Drag: Lost Decade of CP Crutch:
Camera Phone SLR-like Super Human Vision Friend: Pixel Hacking Past: Photon Hacking Now: Future: Immature Mature Expectations Burst Mode Tricks Delights Lost Decade of CP Drag:
Sanchez, Heshmat, Reza, Romberg, Raskar, NATURE Comm 2016
Sanchez, Heshmat, Reza, Romberg, Raskar 2015
Mohit, Shah, Angelino, Sinha
Huang, Wetzstein, Barsky, Raskar. SIGGRAPH 2014. Eyeglasses-free Display: Towards Correcting Visual Aberrations with Computational Light Field Displays.
EyeSelfie, Swedish et al SIGGRAPH 2015
Replacement Expansion Prediction Predictive Modeling of Future Health
OpenDR: API for Diabetic Retinopathy Screening Swedish et al
FLIM Satat et al. Nature Comm 2015 Bhandari et al. 2015
Unbounded HDR using a Modulo Camera Zhao, Shi, Fernandez-Cull, Sai-Kit Yeung, Raskar ICCP 2015
Visual Urban Sensing Naik, Glaeser, Hidalgo, Raskar (email: naik@mit.edu) Crowdsourcing Computer Vision + 3.6/10 Sentiment Analysis for Street-level Images Quantifying Urban Change Applications: Real estate, Data-driven urban policy and planning Decline Improvement streetscore.media.mit.edu How to use computer vision and crowdsourcing to evaluate the appearance of cities across the world at street-level resolution and global scale?
Health and Wellness Interfaces
Raskar, Camera Culture, MIT Media Lab http://cameraculture.info http://Fb.com/cameraculture MIT Media Lab
Bit Hacking Photon Hacking Computer Vision Optics Sensors Signal Processing Displays Machine Learning Computational Light Transport Computational Photography Illumination
• Post-capture control • Emulate studio lights with compact flash • Focus and motion blur • New forms • Flat camera, large LCDs as cameras • Shallow DoF with small aperture • Understand the world • Real or fake • Place 2D photo into 3D • Augmented Reality • Sharing • Lifelogs auto summary • Privacy/Verification • 6D photoframes • Essence • New visual arts Computational Photography Wish List S e n s o r
Camera Phone SLR-like Super Human Vision Friend: Pixel Hacking Past: Photon Hacking Now: Physics Hacking Experience Hacking Future: Emotion Hacking Essence Hacking Immature Mature Expectations Burst Mode Tricks HyperData+ML Delights Lost Decade of Computational Photography 2008-2018 Glorious Decade of Computational Photography 2020’s Drag:

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Lost Decade of Computational Photography

  • 1. Camera Culture Ramesh Raskar Ramesh Raskar Asso. Professor MIT Media Lab Computational Photography Lost Decade .. Glorious Ahead?
  • 2. Friend: Past: Now: Future: Immature Mature Expectations Delights Drag: Tricks Crutch:
  • 3. SLR-like Friend: Past: Now: Future: Immature Mature Expectations Delights Drag: Super Human Vision Crutch:
  • 4. Camera Phone SLR-like Super Human Vision Friend: Pixel Hacking Past: Now: Future: Immature Mature Expectations Burst Mode Tricks Epsilon Photography Delights Drag: Lost Decade of Computational Photography
  • 5. Goal and Experience Low Level Mid Level High Level Hyper realism Raw Angle, spectrum aware Non-visual Data, GPS Metadata Priors Capture Process Comprehensive 8D reflectance field Digital Epsilon Coded Essence CP aims to make progress on both axis Camera Array HDR, FoV Focal stack Decompositio n 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
  • 6. Future of Imaging • What will a camera/display look like in 20 years? • How will the next billion cameras change the social culture? • How can we augment the camera to support best ‘image search’? • How will portable health diagnostics impact healthcare? • Will we live mostly in virtual/augmented reality, telepresent? TiVo for life? • How will ultra-high-speed/resolution imaging change us? • How can we improve ‘trust’ in imaging? • Can we print anything .. Cars, food, .. babies .. (Amazon?) • What are the opportunities in pervasive recording? e.g. GoogleEarth Live • What will be in Photoshop2030? • What is the future of movie-making, news reporting, sports viewing?
  • 7. Camera of the Future Cloth Blimp/Drone Flat-cam Retinal Implant Disappear into gadgets
  • 8. Displays Glasses free 3D Lighting Sensitive Holograms Touch + 3D Hover High Frame Rate Multi-Color Glasses free 2D
  • 9.
  • 10. Can you see around corners ?
  • 13. Heshmat, Lee, Raskar,, Nature Scientific Reports, 2016 Optical Brush
  • 14. Camera Phone SLR-like Super Human Vision Friend: Pixel Hacking Past: Now: Future: Immature Mature Expectations Burst Mode Tricks Delights Drag: Lost Decade of CP Crutch:
  • 15. Camera Phone SLR-like Super Human Vision Friend: Pixel Hacking Past: Photon Hacking Now: Future: Immature Mature Expectations Burst Mode Tricks Delights Lost Decade of CP Drag:
  • 16.
  • 17. Sanchez, Heshmat, Reza, Romberg, Raskar, NATURE Comm 2016
  • 18.
  • 19. Sanchez, Heshmat, Reza, Romberg, Raskar 2015
  • 21.
  • 22. Huang, Wetzstein, Barsky, Raskar. SIGGRAPH 2014. Eyeglasses-free Display: Towards Correcting Visual Aberrations with Computational Light Field Displays.
  • 24. Replacement Expansion Prediction Predictive Modeling of Future Health
  • 25. OpenDR: API for Diabetic Retinopathy Screening Swedish et al
  • 26. FLIM Satat et al. Nature Comm 2015 Bhandari et al. 2015
  • 27. Unbounded HDR using a Modulo Camera Zhao, Shi, Fernandez-Cull, Sai-Kit Yeung, Raskar ICCP 2015
  • 28.
  • 29. Visual Urban Sensing Naik, Glaeser, Hidalgo, Raskar (email: naik@mit.edu) Crowdsourcing Computer Vision + 3.6/10 Sentiment Analysis for Street-level Images Quantifying Urban Change Applications: Real estate, Data-driven urban policy and planning Decline Improvement streetscore.media.mit.edu How to use computer vision and crowdsourcing to evaluate the appearance of cities across the world at street-level resolution and global scale?
  • 30.
  • 31. Health and Wellness Interfaces
  • 32.
  • 33. Raskar, Camera Culture, MIT Media Lab http://cameraculture.info http://Fb.com/cameraculture MIT Media Lab
  • 34. Bit Hacking Photon Hacking Computer Vision Optics Sensors Signal Processing Displays Machine Learning Computational Light Transport Computational Photography Illumination
  • 35. • Post-capture control • Emulate studio lights with compact flash • Focus and motion blur • New forms • Flat camera, large LCDs as cameras • Shallow DoF with small aperture • Understand the world • Real or fake • Place 2D photo into 3D • Augmented Reality • Sharing • Lifelogs auto summary • Privacy/Verification • 6D photoframes • Essence • New visual arts Computational Photography Wish List S e n s o r
  • 36. Camera Phone SLR-like Super Human Vision Friend: Pixel Hacking Past: Photon Hacking Now: Physics Hacking Experience Hacking Future: Emotion Hacking Essence Hacking Immature Mature Expectations Burst Mode Tricks HyperData+ML Delights Lost Decade of Computational Photography 2008-2018 Glorious Decade of Computational Photography 2020’s Drag:

Editor's Notes

  1. http://en.wikipedia.org/wiki/Kano_model
  2. http://en.wikipedia.org/wiki/Kano_model
  3. http://en.wikipedia.org/wiki/Kano_model
  4. Superman can fly and heroes can becomes invisible .. But what about a new power for a future super-hero .. To see around corners ..
  5. My idea is to use the multiple bounces of light i.e. echoes of light. Echoes of sound -> Echoes of light We all know about echoes of sound. But sounds travels slow and we can actually hear the echoes Light travels fast so we need specialized hardware to ‘listen’ to these echoes. So we end up using light sources and cameras that run at a trillion frames per second (not a million and not a billion, but trillion)
  6. Refl.mode THz TDS is actually ToF Advantages: high time res. Ideal case and its challenges Reading through a closed book
  7. http://en.wikipedia.org/wiki/Kano_model
  8. http://en.wikipedia.org/wiki/Kano_model
  9. Ancient text witout damaging books
  10. Provide oral health solutions via a tooth brush that takes achieves x-ray like screening
  11. Siggraph 2015 Paper: EyeSelfie: Swedish, Roesch, …, Raskar
  12. Detect cancer
  13. My work involves creative new ways to play with light by co-designing optical and digital processing. My work lies at the INTERSECTION of processing of photons and processing of bits. At MERL, I transformed the field of computational photography, with key papers and impact on products At Media Lab, I invented a new field ‘computational light transport’
  14. http://cameraculture.info http://raskar.info
  15. http://en.wikipedia.org/wiki/Kano_model