Name
Abstract | ||
|---|---|---|
To recover the three dimensional (3D) volumetric distribution of matter in an object, images of the object are captured from multiple directions and locations. Using these images tomographic computations extract the distribution. In highly scattering media and constrained, natural irradiance, tomography must explicitly account for off-axis scattering. Furthermore, the tomographic model and 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. Moreover, the model is highly parallelizable in our formulation. This enables 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 (RT) modeling in-situ. |
Year | Venue | Field |
|---|---|---|
2015 | CoRR | Parallelizable manifold,Computer vision,In situ,Medical imaging,Computer science,Tomography,Scattering,Artificial intelligence,Radiative transfer,Rendering (computer graphics),Computation |
DocType | Volume | Citations |
Journal | abs/1512.02110 | 0 |
PageRank | References | Authors |
0.34 | 10 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| vadim holodovsky | 1 | 1 | 1.37 |
| Yoav Y. Schechner | 2 | 629 | 58.12 |
| Anat Levin | 3 | 3578 | 212.90 |
| aviad levis | 4 | 1 | 1.70 |
| Amit Aides | 5 | 17 | 3.30 |