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Project

Looking Around Corners

Creative Commons

Attribution 4.0 International

Camera Culture | MIT Media Lab

Groups

Using a femtosecond laser and a camera with a time resolution of about one trillion frames per second, we recover objects hidden out of sight. We measure speed-of-light timing information of light scattered by the hidden objects via diffuse surfaces in the scene. The object data are mixed up and are difficult to decode using traditional cameras. We combine this "time-resolved" information with novel reconstruction algorithms to untangle image information and demonstrate the ability to look around corners.

The seemingly impossible task of recording what is beyond the line of sight is possible due to ultra‐fast imaging. A new form of photography, Femto-photography exploits the finite speed of light and analyzes ‘echoes of light’.

Laser pulse shooting through a bottle and visualized at a trillion frames per second

Multi-path analysis: We show that by emitting short pulses and analyzing multi-bounce reflection from the door, we can infer hidden geometry even if the intermediate bounces are not visible. The transient imaging camera prototype consists of (a) Femtosecond laser illumination (b) Picosecond-accurate camera and (c) inversion algorithm. We measure the five dimensional Space Time Impulse Response (STIR) of the scene and reconstruct the hidden surface.

Higher Dimensional Light Transport: Popular imaging methods plotted in the Space-Angle-Time axes. With higher dimensional light capture, we expand the horizons of scene understanding. Our work uses LIDAR-like imaging hardware, but, in contrast, we exploit the multi-path information which is rejected in both LIDAR and OCT.

Paper

A. Velten, T. Willwacher, O. Gupta, A. Veeraraghavan, M. G. Bawendi, and R. Raskar, “Recovering ThreeDimensional Shape around a Corner using Ultra-Fast Time-of-Flight Imaging.” Nature Communications, March 2012, http://dx.doi.org/10.1038/ncomms1747

corner-prep.pdf (PDF)

Team

Ramesh Raskar, Associate Professor, MIT Media Lab; Project Director (raskar(at)mit.edu)

Moungi G. Bawendi, Professor, Dept of Chemistry, MIT

Andreas Velten, Postdoctoral Associate, MIT Media Lab; Lead Author (velten(at)mit.edu)

Christopher Barsi, Postdoctoral Associate, MIT Media Lab

Everett Lawson, MIT Media Lab

Nikhil Naik, Research Assistant, MIT Media Lab

Otkrist Gupta, Research Assistant, MIT Media Lab

Thomas Willwacher, Harvard University

Ashok Veeraraghavan, Rice University

Amy Fritz, MIT Media Lab

Chinmaya Joshi, MIT Media Lab and COE-Pune


Current Collaborators:

Diego Gutierrez, Universidad de Zaragoza

Di Wu, MIT Media Lab and Tsinghua U.

Matt O’toole, MIT Media Lab and U. of Toronto

Belen Masia, MIT Media Lab and Universidad de Zaragoza

Kavita Bala, Cornell U.

Shuang Zhao, Cornell U.

FAQ

  1. How can Femto-photography see what is beyond the line of sight?

    Femto-photography exploits the finite speed of light and works like an ultra-fast time of flight camera. In traditional photography, the speed of light is infinite and does not play a role. In our transient light transport framework, the finite amount of time light takes to travel from one surface to another provides useful information. The key contribution is a computational tool of transient reasoning for the inversion of light transport. The basic concept of a transient imaging camera can be understood using a simple example of a room with an open door. The goal here is to compute the geometry of the object inside the room by exploiting light reflected off the door. The user directs an ultra short laser beam onto the door and after the first bounce the beam scatters into the room. The light reflects from objects inside the room and again from the door back toward the transient imaging camera. An ultra-fast array of detectors measures the time profile of the returned signal from multiple positions on the door. We analyze this multi‐path light transport and infer shapes of objects that are in direct sight as well as beyond the line of sight. The analysis of the onsets in the time profile indicates the shape; we call this the inverse geometry problem.

More Info can be found on the Camera Culture website

Research Topics
#robotics #computer vision #augmented reality #imaging