Bayesian View Synthesis and Image-Based Rendering ... - HAL-Inria
Top: Numerical results for synthetic and real-world light fields. ... the proposed method are visibly sharper, in particular along color edges. tarot (CD) couple (CD).
Inria - PRIMA Team, Univ. Grenoble Alpes, LIG, F-38000 Grenoble, France, CNRS, LIG, F-38000 Grenoble, France.
University of Konstanz
Image-Based Rendering
Previous and Proposed Generative Model
Equations Notation
Maximum a Posteriori energy
Target image: u : Γ → R (or R3)
Minimization with a re-weighted iterative method
Relation between image error on geometric error
• Use estimate u˜ of u and consider ωi(˜ u) constant during iteration: the simplified energy is convex.
E(u) = Edata(u) + λEprior(u)
Input images: vi : Ωi → R (or R3)
n Z X 1 ωi(u) mi(b ∗ (u ◦ τi) − vi)2 dx, Edata(u) = 2 Ωi i=1 −1 with ωi(u) = σs2 + σg2i
Backwards warp: τi : Ωi → Γ Binary occlusion mask: mi : Ωi → {0, 1} Visibility set: Vi ⊂ Ωi | mi = 1
∂(u ◦ τi) ∂τi σgi = σzi b ∗ = σzi b ∗ (∇u ◦ τi)· ∂z ∂z i
i
Z
Forward warp: βi : τi(Vi) → Ωi | βi ◦ τi = Id
Eprior(u) =
• Minimize simplified energy using FISTA. • Update weights with current solution and iterate. P 1 First iteration: u˜ = n vi ◦ βi Code available at http://sourceforge.net/projects/cocolib/
|Du| Γ
Example for Intuition
Numerical and Visual Results HCI light fields, gantry maria couple
Top: Numerical results for synthetic and real-world light fields. We compare our method to Wanner et al. ECCV 2012 work with respect to same-resolution view synthesis for estimated disparity and a flat plane proxy, as well as super-resolved view synthesis. For each light field, the first value is the PSNR (bigger is better), the second value is DSSIM in units of 10−4 (smaller is better). The better value is highlighted in bold. Right: Visual comparison of novel views obtained for different light fields. From top to bottom, the rows present closeups of the ground truth images, the results obtained by Wanner et al. ECCV 2012 work, and our results. CD stands for computed disparity, PD for planar disparity and SR for super-resolution. The results obtained by the proposed method are visibly sharper, in particular along color edges.
deal with spatial super-resolution in the multi-view and light field setting [3, 10], .... derive how this distribution generates a color error distribu- tion in the image ...
In figures 2 to 7, we show for each data set the ground-truth image (if available), the disparity map used for novel view synthesis, the view generated with the ...
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