ICCV 2013
In this supplemental material, we woud like to showcase our algorithm's robustness both visually and numerically. We are working with noisy and low resolution light-field data. Using both defocus and correspondence depth cues produces better results compared to other methods. The variety of images shows how our algorithm can perform robustly under complex shapes, planes with gradual depth changes, and noise, banding, moire, and repeating patterns.
This supplementary material contains two comparison sections: examples and occlusion boundaries.
Examples
This section shows how our algorithm performs well across different scenarios by utilizing both defocus and correspondence cues.
Yellow Flower |
Input Central View Image* |
Defocus Only |
Correspondence Only |
Our Method |
Sun et al. 2010 [1] |
Wanner et al. 2012 [2] |
Purple Plant |
Input Central View Image* |
Defocus Only |
Correspondence Only |
Our Method |
Sun et al. 2010 [1] |
Wanner et al. 2012 [2] |
Input Central View Image* |
Defocus Only |
Correspondence Only |
Our Method |
Sun et al. 2010 [1] |
Wanner et al. 2012 [2] |
Input Central View Image* |
Defocus Only |
Correspondence Only |
Our Method |
Sun et al. 2010 [1] |
Wanner et al. 2012 [2] |
Leaf 1 |
Input Central View Image* |
Defocus Only |
Correspondence Only |
Our Method |
Sun et al. 2010 [1] |
Wanner et al. 2012 [2] |
Input Central View Image* |
Defocus Only |
Correspondence Only |
Our Method |
Sun et al. 2010 [1] |
Wanner et al. 2012 [2] |
Input Central View Image* |
Defocus Only |
Correspondence Only |
Our Method |
Sun et al. 2010 [1] |
Wanner et al. 2012 [2] |
Input Central View Image* |
Defocus Only |
Correspondence Only |
Our Method |
Sun et al. 2010 [1] |
Wanner et al. 2012 [2] |
Stump |
Input Central View Image* |
Defocus Only |
Correspondence Only |
Our Method |
Sun et al. 2010 [1] |
Wanner et al. 2012 [2] |
Leaf 2 |
Input Central View Image* |
Defocus Only |
Correspondence Only |
Our Method |
Sun et al. 2010 [1] |
Wanner et al. 2012 [2] |
Input Central View Image* |
Defocus Only |
Correspondence Only |
Our Method |
Sun et al. 2010 [1] |
Wanner et al. 2012 [2] |
Input Central View Image* |
Defocus Only |
Correspondence Only |
Our Method |
Sun et al. 2010 [1] |
Wanner et al. 2012 [2] |
Input Central View Image* |
Defocus Only |
Correspondence Only |
Our Method |
Sun et al. 2010 [1] |
Wanner et al. 2012 [2] |
Input Central View Image* |
Defocus Only |
Correspondence Only |
Our Method |
Sun et al. 2010 [1] |
Wanner et al. 2012 [2] |
Decorative Flowers |
Input Central View Image* |
Defocus Only |
Correspondence Only |
Our Method |
Sun et al. 2010 [1] |
Wanner et al. 2012 [2] |
Shoe |
Input Central View Image* |
Defocus Only |
Correspondence Only |
Our Method |
Sun et al. 2010 [1] |
Wanner et al. 2012 [2] |
Input Central View Image* |
Defocus Only |
Correspondence Only |
Our Method |
Sun et al. 2010 [1] |
Wanner et al. 2012 [2] |
Input Central View Image* |
Defocus Only |
Correspondence Only |
Our Method |
Sun et al. 2010 [1] |
Wanner et al. 2012 [2] |
Occlusion Boundaries
This section shows how our algorithm performs consistently through multiple examples in detecting occlusion boundaries, yielding higher precision and recall rates.
Input Central View Image* |
User-Drawn Occlussion Boundaries |
Our Occlusion Boundaries |
Sun et al. 2010 Occlusion Boundaries [1] |
Wanner et al. 2012 Occlusion Boundaries [2] |
|
Precision: 0.86 Recall: 0.83 |
Precision: 0.65 Recall: 0.57 |
Precision: 0.44 Recall: 0.76 |
|||
Our Depth Estimation |
Sun et al. 2010 Depth Estimation [1] |
Wanner et al. 2012 Depth Estimation [2] |
Input Central View Image* |
User-Drawn Occlussion Boundaries |
Our Occlusion Boundaries |
Sun et al. 2010 Occlusion Boundaries [1] |
Wanner et al. 2012 Occlusion Boundaries [2] |
|
Precision: 0.71 Recall: 0.87 |
Precision: 0.43 Recall: 0.19 |
Precision: 0.27 Recall: 0.52 |
|||
Our Depth Estimation |
Sun et al. 2010 Depth Estimation [1] |
Wanner et al. 2012 Depth Estimation [2] |
Input Central View Image* |
User-Drawn Occlussion Boundaries |
Our Occlusion Boundaries |
Sun et al. 2010 Occlusion Boundaries [1] |
Wanner et al. 2012 Occlusion Boundaries [2] |
|
Precision: 0.68 Recall: 0.97 |
Precision: 0.52 Recall: 0.34 |
Precision: 0.22 Recall: 0.11 |
|||
Our Depth Estimation |
Sun et al. 2010 Depth Estimation [1] |
Wanner et al. 2012 Depth Estimation [2] |
Input Central View Image* |
User-Drawn Occlussion Boundaries |
Our Occlusion Boundaries |
Sun et al. 2010 Occlusion Boundaries [1] |
Wanner et al. 2012 Occlusion Boundaries [2] |
|
Precision: 0.70 Recall: 0.94 |
Precision: 0.68 Recall: 0.28 |
Precision: 0.55 Recall: 0.52 |
|||
Our Depth Estimation |
Sun et al. 2010 Depth Estimation [1] |
Wanner et al. 2012 Depth Estimation [2] |
Input Central View Image* |
User-Drawn Occlussion Boundaries |
Our Occlusion Boundaries |
Sun et al. 2010 Occlusion Boundaries [1] |
Wanner et al. 2012 Occlusion Boundaries [2] |
|
Precision: 0.72 Recall: 0.77 |
Precision: 0.74 Recall: 0.52 |
Precision: 0.63 Recall: 0.30 |
|||
Our Depth Estimation |
Sun et al. 2010 Depth Estimation [1] |
Wanner et al. 2012 Depth Estimation [2] |
* Input central view images are generated using our light-field processing engine
[1] D. Sun, S. Roth, and M. Black. Secrets of optical flow estimation and their principles. In CVPR, 2010.
[2] S. Wanner and B. Goldluecke. Globally consistent depth labeling of 4D light fields. In CVPR, 2012.
All images are shot with the Lytro Camera in multiple scenarios such as ISO, outdoors/indoors, focal length, and exposure.