In-situ multi-view multi-scattering stochastic tomography

Vadim Holodovsky; Yoav Y. Schechner; Anat Levin; Aviad Levis; Amit Aides

doi:10.1109/ICCPHOT.2016.7492869

In-situ multi-view multi-scattering stochastic tomography

Vadim Holodovsky, Yoav Y. Schechner, Anat Levin, Aviad Levis, Amit Aides

Technion - Israel Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

To recover the three dimensional (3D) volumetric matter distribution in an object, the object is imaged from multiple directions and locations. Using these images, tomographic computations seek the distribution. When scattering is significant and under constrained irradiance, tomography must explicitly account for off-axis scattering. Furthermore, tomographic recovery must function when imaging is done in-situ, as occurs in medical imaging and ground-based atmospheric sensing. We formulate tomography that handles arbitrary orders of scattering, using a Monte-Carlo model. The model is highly parallelizable in our formulation. This can enable large scale rendering and recovery of volumetric scenes having a large number of variables. We solve stability and conditioning problems that stem from radiative transfer modeling in-situ.

Original language	English
Title of host publication	2016 IEEE International Conference on Computational Photography, ICCP 2016 - Proceedings
ISBN (Electronic)	9781467386234
DOIs	https://doi.org/10.1109/ICCPHOT.2016.7492869
State	Published - 15 Jun 2016
Event	2016 IEEE International Conference on Computational Photography, ICCP 2016 - Evanston, United States Duration: 13 May 2016 → 15 May 2016

Publication series

Name	2016 IEEE International Conference on Computational Photography, ICCP 2016 - Proceedings

Conference

Conference	2016 IEEE International Conference on Computational Photography, ICCP 2016
Country/Territory	United States
City	Evanston
Period	13/05/16 → 15/05/16

All Science Journal Classification (ASJC) codes

Media Technology
Instrumentation

Access to Document

10.1109/ICCPHOT.2016.7492869

Cite this

Holodovsky, V., Schechner, Y. Y., Levin, A., Levis, A., & Aides, A. (2016). In-situ multi-view multi-scattering stochastic tomography. In 2016 IEEE International Conference on Computational Photography, ICCP 2016 - Proceedings Article 7492869 (2016 IEEE International Conference on Computational Photography, ICCP 2016 - Proceedings). https://doi.org/10.1109/ICCPHOT.2016.7492869

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title = "In-situ multi-view multi-scattering stochastic tomography",

abstract = "To recover the three dimensional (3D) volumetric matter distribution in an object, the object is imaged from multiple directions and locations. Using these images, tomographic computations seek the distribution. When scattering is significant and under constrained irradiance, tomography must explicitly account for off-axis scattering. Furthermore, tomographic recovery must function when imaging is done in-situ, as occurs in medical imaging and ground-based atmospheric sensing. We formulate tomography that handles arbitrary orders of scattering, using a Monte-Carlo model. The model is highly parallelizable in our formulation. This can enable large scale rendering and recovery of volumetric scenes having a large number of variables. We solve stability and conditioning problems that stem from radiative transfer modeling in-situ.",

author = "Vadim Holodovsky and Schechner, \{Yoav Y.\} and Anat Levin and Aviad Levis and Amit Aides",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Conference on Computational Photography, ICCP 2016 ; Conference date: 13-05-2016 Through 15-05-2016",

year = "2016",

month = jun,

day = "15",

doi = "10.1109/ICCPHOT.2016.7492869",

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series = "2016 IEEE International Conference on Computational Photography, ICCP 2016 - Proceedings",

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Holodovsky, V, Schechner, YY , Levin, A, Levis, A & Aides, A 2016, In-situ multi-view multi-scattering stochastic tomography. in 2016 IEEE International Conference on Computational Photography, ICCP 2016 - Proceedings., 7492869, 2016 IEEE International Conference on Computational Photography, ICCP 2016 - Proceedings, 2016 IEEE International Conference on Computational Photography, ICCP 2016, Evanston, United States, 13/05/16. https://doi.org/10.1109/ICCPHOT.2016.7492869

In-situ multi-view multi-scattering stochastic tomography. / Holodovsky, Vadim; Schechner, Yoav Y.; Levin, Anat et al.
2016 IEEE International Conference on Computational Photography, ICCP 2016 - Proceedings. 2016. 7492869 (2016 IEEE International Conference on Computational Photography, ICCP 2016 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - In-situ multi-view multi-scattering stochastic tomography

AU - Holodovsky, Vadim

AU - Schechner, Yoav Y.

AU - Levin, Anat

AU - Levis, Aviad

AU - Aides, Amit

PY - 2016/6/15

Y1 - 2016/6/15

N2 - To recover the three dimensional (3D) volumetric matter distribution in an object, the object is imaged from multiple directions and locations. Using these images, tomographic computations seek the distribution. When scattering is significant and under constrained irradiance, tomography must explicitly account for off-axis scattering. Furthermore, tomographic recovery must function when imaging is done in-situ, as occurs in medical imaging and ground-based atmospheric sensing. We formulate tomography that handles arbitrary orders of scattering, using a Monte-Carlo model. The model is highly parallelizable in our formulation. This can enable large scale rendering and recovery of volumetric scenes having a large number of variables. We solve stability and conditioning problems that stem from radiative transfer modeling in-situ.

AB - To recover the three dimensional (3D) volumetric matter distribution in an object, the object is imaged from multiple directions and locations. Using these images, tomographic computations seek the distribution. When scattering is significant and under constrained irradiance, tomography must explicitly account for off-axis scattering. Furthermore, tomographic recovery must function when imaging is done in-situ, as occurs in medical imaging and ground-based atmospheric sensing. We formulate tomography that handles arbitrary orders of scattering, using a Monte-Carlo model. The model is highly parallelizable in our formulation. This can enable large scale rendering and recovery of volumetric scenes having a large number of variables. We solve stability and conditioning problems that stem from radiative transfer modeling in-situ.

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DO - 10.1109/ICCPHOT.2016.7492869

M3 - منشور من مؤتمر

T3 - 2016 IEEE International Conference on Computational Photography, ICCP 2016 - Proceedings

BT - 2016 IEEE International Conference on Computational Photography, ICCP 2016 - Proceedings

T2 - 2016 IEEE International Conference on Computational Photography, ICCP 2016

Y2 - 13 May 2016 through 15 May 2016

ER -

In-situ multi-view multi-scattering stochastic tomography

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