Bayesian View Synthesis and Image-Based Rendering Principles
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 ...
Bayesian View Synthesis and Image-Based Rendering Principles Supplementary material Sergi Pujades, Fr´ed´eric Devernay Inria - PRIMA Team, Univ. Grenoble Alpes, LIG, F-38000 Grenoble, France, CNRS, LIG, F-38000 Grenoble, France. Bastian Goldluecke Heidelberg Collaboratory for Image Processing 1. Results We present the full resolution images corresponding to the closeups in figure 4 in the paper, reproduced here as figure 1. 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 approach in Wanner et al. [26], as well as the view generated by the proposed method. Results of the previous method where obtained using the public released implementation of the code available at http://sourceforge.net/projects/cocolib/.
couple (CD)
buddha (PD)
maria (PD)
still life (SR)
truck (SR)
Proposed
Wanner et al.
Original
tarot (CD)
Figure 1. 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., and our results. CD stands for computed disparity, PD for planar disparity and SR for super-resolution, see text for details. Full resolution images can be found in the additional material. The results obtained by the proposed method are visibly sharper, in particular along color edges. 1
Original
Estimated disparity map
Previous
Proposed
Figure 2. Novel view of the Stanford gantry data set “Tarot” (fine configuration). Synthesized at x1 resolution using the estimated disparity map.
Original
Estimated disparity map
Previous
Proposed
Figure 3. Novel view of the HCI gantry data set “Couple”. Synthesized at x1 resolution using the estimated disparity map.
Original
Flat geometric proxy
Previous
Proposed
Figure 4. Novel view of the HCI raytraced data set “Buddha”. Synthesized at x1 resolution using a plane in the center of the scene as geometric proxy.
Original
Flat geometric proxy
Previous
Proposed
Figure 5. Novel view of the HCI gantry data set “Maria”. Synthesized at x1 resolution using a plane in the center of the scene as geometric proxy.
Original
Estimated disparity map
Previous
Proposed
Figure 6. Novel view of the HCI gantry data set “Still life”. Synthesized at x3 resolution using the estimated disparity map. An original hi-resolution image is not available, so we present the low-resolution version of the corresponding view.
Original
Estimated disparity map
Previous
Proposed
Figure 7. Novel view of the Stanford gantry data set “Truck”. Synthesized at x3 resolution using the estimated disparity map. An original hi-resolution image is not available, so we present the low-resolution version of the corresponding view.
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 ...
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).
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