Decomposing Global Light Transport Using Time of Flight Imaging

Wu, Di; Velten, Andreas; O’Toole, Matthew; Masiá, Belén; Agrawal, Amit; Dai, Qionghai; Raskar, Ramesh

doi:10.1007/s11263-013-0668-2

Cited by 87 publications

(65 citation statements)

References 28 publications

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“…Starting with Kirani et al's original work [11], there have been several proposals to use transient images to capture surface reflectance [14], or simply to visualize light transport in complex environments to gain a better understanding of optical phenomena [21]. Wu et al [22] proposed to use transient images together with models of light/object interaction to factor the illumination into direct and indirect components.…”

Section: Related Workmentioning

confidence: 99%

Diffuse Mirrors: 3D Reconstruction from Diffuse Indirect Illumination Using Inexpensive Time-of-Flight Sensors

Heide

Xiao

Heidrich

et al. 2014

2014 IEEE Conference on Computer Vision and Pattern Recognition

161

138

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The functional difference between a diffuse wall and a mirror is well understood: one scatters back into all directions, and the other one preserves the directionality of reflected light. The temporal structure of the light, however, is left intact by both: assuming simple surface reflection, photons that arrive first are reflected first. In this paper, we exploit this insight to recover objects outside the line of sight from second-order diffuse reflections, effectively turning walls into mirrors. We formulate the reconstruction task as a linear inverse problem on the transient response of a scene, which we acquire using an affordable setup consisting of a modulated light source and a time-of-flight image sensor. By exploiting sparsity in the reconstruction domain, we achieve resolutions in the order of a few centimeters for object shape (depth and laterally) and albedo. Our method is robust to ambient light and works for large room-sized scenes. It is drastically faster and less expensive than previous approaches using femtosecond lasers and streak cameras, and does not require any moving parts.

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Section: Related Workmentioning

confidence: 99%

Diffuse Mirrors: 3D Reconstruction from Diffuse Indirect Illumination Using Inexpensive Time-of-Flight Sensors

Heide

Xiao

Heidrich

et al. 2014

2014 IEEE Conference on Computer Vision and Pattern Recognition

161

138

View full text Add to dashboard Cite

show abstract

“…Since the transient image underlies physical constraints of light transport in a scene, it is reasonable to also consider physically based models with a small number of parameters. 23 The use of such models for regularization turns the problem non-convex and calls for specialized solvers, unless the number of parameters is small enough to use a basis pursuit approach. In recent work, we introduced the use of exponentially modified Gaussian distributions in a sparse convolutional framework to reconstruct temporally dense pixel responses.…”

Section: Reconstructing Transient Images From Amcw Measurementsmentioning

confidence: 99%

Computational imaging of light in flight

Hullin

2014

SPIE Proceedings

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Many computer vision tasks are hindered by image formation itself, a process that is governed by the so-called plenoptic integral. By averaging light falling into the lens over space, angle, wavelength and time, a great deal of information is irreversibly lost. The emerging idea of transient imaging operates on a time resolution fast enough to resolve non-stationary light distributions in real-world scenes. It enables the discrimination of light contributions by the optical path length from light source to receiver, a dimension unavailable in mainstream imaging to date. Until recently, such measurements used to require high-end optical equipment and could only be acquired under extremely restricted lab conditions. To address this challenge, we introduced a family of computational imaging techniques operating on standard time-of-flight image sensors, for the first time allowing the user to "film" light in flight in an affordable, practical and portable way. Just as impulse responses have proven a valuable tool in almost every branch of science and engineering, we expect light-in-flight analysis to impact a wide variety of applications in computer vision and beyond.

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“…Applications using ToF cameras include 3D scene reconstruction [6,23], reflectance capture using ultrafast imaging [20], frequency analysis to address multipath interference [5,13,26] and human pose detection/tracking [7,8]. These depth cameras are increasingly common in various application domains such as intelligent vehicles, robotic environment mapping and surveillance.…”

Section: Related Workmentioning

confidence: 99%

Phase messaging method for time-of-flight cameras

Yuan

Howard

Dana

et al. 2014

2014 IEEE International Conference on Computational Photography (ICCP)

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Ubiquitous light emitting devices and low-cost commercial digital cameras facilitate optical wireless communication system such as visual MIMO where handheld cameras communicate with electronic displays. While intensity-based optical communications are more prevalent in camera-display messaging, we present a novel method that uses modulated light phase for messaging and timeof-flight (ToF) cameras for receivers. With intensity-based methods, light signals can be degraded by reflections and ambient illumination. By comparison, communication using ToF cameras is more robust against challenging lighting conditions. Additionally, the concept of phase messaging can be combined with intensity messaging for a significant data rate advantage. In this work, we design and construct a phase messaging array (PMA), which is the first of its kind, to communicate to a ToF depth camera by manipulating the phase of the depth camera's infrared light signal. The array enables message variation spatially using a plane of infrared light emitting diodes and temporally by varying the induced phase shift. In this manner, the phase messaging array acts as the transmitter by electronically controlling the light signal phase. The ToF camera acts as the receiver by observing and recording a time-varying depth. We show a complete implementation of a 3 ⇥ 3 prototype array with custom hardware and demonstrating average bit accuracy as high as 97.8%. The prototype data rate with this approach is 1 Kbps that can be extended to approximately 10 Mbps.

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Decomposing Global Light Transport Using Time of Flight Imaging

Cited by 87 publications

References 28 publications

Diffuse Mirrors: 3D Reconstruction from Diffuse Indirect Illumination Using Inexpensive Time-of-Flight Sensors

Diffuse Mirrors: 3D Reconstruction from Diffuse Indirect Illumination Using Inexpensive Time-of-Flight Sensors

Computational imaging of light in flight

Phase messaging method for time-of-flight cameras

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