Single Lens Stereo with a Plenoptic Camera
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Depth from Defocus vs. Stereo: How Different Really Are They?
International Journal of Computer Vision - Special issue on computer vision research at the Technion
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
ACM SIGGRAPH 2005 Papers
Reconstructing Occluded Surfaces Using Synthetic Apertures: Stereo, Focus and Robust Measures
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
ACM SIGGRAPH 2007 papers
Image and depth from a conventional camera with a coded aperture
ACM SIGGRAPH 2007 papers
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
Understanding Camera Trade-Offs through a Bayesian Analysis of Light Field Projections
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
4D frequency analysis of computational cameras for depth of field extension
ACM SIGGRAPH 2009 papers
Spatio-angular resolution tradeoffs in integral photography
EGSR'06 Proceedings of the 17th Eurographics conference on Rendering Techniques
Adaptive coded aperture photography
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
Coding Depth through Mask Structure
Computer Graphics Forum
Depth and deblurring from a spectrally-varying depth-of-field
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Optimized aperture shapes for depth estimation
Pattern Recognition Letters
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Computational depth estimation is a central task in computer vision and graphics. A large variety of strategies have been introduced in the past relying on viewpoint variations, defocus changes and general aperture codes. However, the tradeoffs between such designs are not well understood. Depth estimation from computational camera measurements is a highly non-linear process and therefore most research attempts to evaluate depth estimation strategies rely on numerical simulations. Previous attempts to design computational cameras with good depth discrimination optimized highly non-linear and nonconvex scores, and hence it is not clear if the constructed designs are optimal. In this paper we address the problem of depth discrimination from J images captured using J arbitrary codes placed within one fixed lens aperture. We analyze the desired properties of discriminative codes under a geometric optics model and propose an upper bound on the best possible discrimination. We show that under a multiplicative noise model, the half ring codes discovered by Zhou et al. [1] are near-optimal. When a large number of images are allowed, a multiaperture camera [2] dividing the aperture into multiple annular rings provides near-optimal discrimination. In contrast, the plenoptic camera of [5] which divides the aperture into compact support circles can achieve at most 50% of the optimal discrimination bound.