|This thesis presents a complete processing pipeline of densely sampled, high resolution light fields, from acquisition to rendering. The key components of the pipeline include 3D scene reconstruction, geometry-driven sampling analysis, and controllable multiscopic 3D rendering.
The thesis first addresses 3D geometry reconstruction from light fields. We show that dense sampling of a scene attained in light fields allows for more robust and accurate depth estimation without resorting to patch matching and costly global optimization processes. Our algorithm estimates the depth for each and every light ray in the light field with great accuracy, and its pixel-wise depth computation results in particularly favorable quality around depth discontinuities. In fact, most operations are kept localized over small portions of the light field, which by itself is crucial to scalability for higher resolution input and also well suited for efficient parallelized implementations. Resulting reconstructions retain fine details of the scene and exhibit precise localization of object boundaries.
While it is the key to the success of our reconstruction algorithm, the dense sampling of light fields entails difficulties when it comes to the acquisition and processing of light fields. This raises a question of optimal sampling density required for faithful geometry reconstruction. Existing works focus more on the alias-free rendering of light fields, and geometry-driven analysis has seen much less research effort. We propose an analysis model for determining sampling locations that are optimal in the sense of high quality geometry reconstruction. This is achieved by analyzing the visibility of scene points and the resolvability of depth and estimating the distribution of reliable estimates over potential sampling locations.
A light field with accurate depth information enables an entirely new approach to flexible and controllable 3D rendering. We develop a novel algorithm for multiscopic rendering of light fields which provides great controllability over the perceived depth conveyed in the output. The algorithm synthesizes a pair of stereoscopic images directly from light fields and allows us to control stereoscopic and artistic constraints on a per-pixel basis. It computes non-planar 2D cuts over a light field volume that best meet described constraints by minimizing an energy functional. The output images are synthesized by sampling light rays on the cut surfaces. The algorithm generalizes for multiscopic 3D displays by computing multiple cuts.
The resulting algorithms are highly relevant to many application scenarios. It can readily be applied to 3D scene reconstruction and object scanning, depth-assisted segmentation, image-based rendering, and stereoscopic content creation and post-processing, and can also be used to improve the quality of light field rendering that requires depth information such as super-resolution and extended depth of field.