TY - GEN
T1 - 4D Cloud Scattering Tomography
AU - Ronen, Roi
AU - Schechner, Yoav Y.
AU - Eytan, Eshkol
N1 - Publisher Copyright: © 2021 IEEE
PY - 2021
Y1 - 2021
N2 - We derive computed tomography (CT) of a time-varying volumetric scattering object, using a small number of moving cameras. We focus on passive tomography of dynamic clouds, as clouds have a major effect on the Earth's climate. State of the art scattering CT assumes a static object. Existing 4D CT methods rely on a linear image formation model and often on significant priors. In this paper, the angular and temporal sampling rates needed for a proper recovery are discussed. Spatiotemporal CT is achieved using gradient-based optimization, which accounts for the correlation time of the dynamic object content. We demonstrate this in physics-based simulations and on experimental real-world data.
AB - We derive computed tomography (CT) of a time-varying volumetric scattering object, using a small number of moving cameras. We focus on passive tomography of dynamic clouds, as clouds have a major effect on the Earth's climate. State of the art scattering CT assumes a static object. Existing 4D CT methods rely on a linear image formation model and often on significant priors. In this paper, the angular and temporal sampling rates needed for a proper recovery are discussed. Spatiotemporal CT is achieved using gradient-based optimization, which accounts for the correlation time of the dynamic object content. We demonstrate this in physics-based simulations and on experimental real-world data.
UR - http://www.scopus.com/inward/record.url?scp=85127798548&partnerID=8YFLogxK
U2 - 10.1109/ICCV48922.2021.00547
DO - 10.1109/ICCV48922.2021.00547
M3 - منشور من مؤتمر
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 5500
EP - 5509
BT - Proceedings - 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021
T2 - 18th IEEE/CVF International Conference on Computer Vision, ICCV 2021
Y2 - 11 October 2021 through 17 October 2021
ER -