On Plenoptic Multiplexing and Reconstruction
Photography has been striving to capture an ever increasing amount of visual information in a single image. Digital sensors, however, are limited to recording a small subset of the desired information at each pixel. A common approach to overcoming the limitations of sensing hardware is the optical multiplexing of high-dimensional data into a photograph. While this is a well-studied topic for imaging with color filter arrays, we develop a mathematical framework that generalizes multiplexed imaging to all dimensions of the plenoptic function. This framework unifies a wide variety of existing approaches to analyze and reconstruct multiplexed data in either the spatial or the frequency domain. We demonstrate many practical applications of our framework including high-quality light field reconstruction, the first comparative noise analysis of light field attenuation masks, and an analysis of aliasing in multiplexing applications.
Three-Dimensional Kaleidoscopic ImagingIlya Reshetouski, Alkhazur Manakov, Hans-Peter Seidel, and Ivo Ihrke
CVPR 2011 (oral)
We introduce three-dimensional kaleidoscopic imaging, a promising alternative for recording multi-view imagery.
The main limitation of multi-view reconstruction techniques is the limited number of views that are available from multi-camera systems, especially for dynamic scenes.
Our new system is based on imaging an object inside a kaleidoscopic mirror system. We show that this approach can generate a large number of high-quality views well distributed over the hemisphere surrounding the object in a single shot. In comparison to existing multi-view systems, our method offers a number of advantages: it is possible to operate with a single camera, the individual views are perfectly synchronized, and they have the same radiometric and colorimetric properties.
We describe the setup both theoretically, and provide methods for a practical implementation. Enabling interfacing to standard multi-view algorithms for further processing is an important goal of our techniques.
Example of labeling process:
|=>||=>||=>||=>||Source image||Silhouette image||Chambers extraction||Visual hull
|Labeling of views|
Supplemental materials [pdf]
Labeling data example (with MatLab loader) [zip]
Labeling of the dynamic scene example: Input movie [mpg], Segmentation movie [mpg], Labeling movie [mpg]