1. A Novel 2D-to-3D Conversion System Using Edge Information
IEEE Transactions on Consumer Electronics 2010
Chao-Chung Cheng
Chung-Te li
Liang-Gee Chen

2. Introduction Some approaches that can generate 3D content
Time-of-flight depth sensor
Triangular stereo vision
3D graph rendering

3. Introduction How does our brain perceive depth?
Monocular cues：one of the major categories for depth perception
Motion parallax
Binocular cues
Monocular Cues involve those cues that exist for a single eye.  This is one of the major categories for depth perception, as there are several different monocular cues that help in depth perception
2.A nother important monocular cue for depth is provided by motion.  As the world moves, whether that movement is caused by the perceiver or by his surrounding environment, objects in the environment move in distinct, predictable patterns.  This concept is known as the motion parallax. 
3.Cues that involve both eyes are called binocular cues, and form the other major category for depth cues.  These cues exist because of the differential location of the two eyes.  The above link explains this phenomenon in more detail.

4. Monocular cues Interposition (overlapping) Relative Height
Familiar Size
Texture Gradient
Shadow
Linear Perspective

5. Block-Based Region Grouping Depth from Prior Hypothesis
Proposed System
Block-Based Region Grouping
Depth from Prior Hypothesis
3D Image Visualization using Bilateral Filtering and Depth Image-Based Rendering

6. Proposed 2D-to-3D Conversion System

7. Block-Based Region Grouping
Measure the similarity of neighboring blocks
The blocks are segmented into multiple groups by MST

8. Depth from Prior Hypothesis
Use a line detection algorithm[9] to detect the linear perspective of the scene
C.-C. Cheng, C.-T. Li, P.-S. Huang, T.-K. Lin, Y.-M. Tsai, and L.-G.
Chen, “A block-based 2D-to-3D conversion system with bilateral filter,”
in Proc. IEEE Int. Conf. Consumer Electronics, 2009

9. Depth from Prior Hypothesis
Find the corresponding depth map gradients
Compute the gravity center of the block group as the depth

10. 3D Image Visualization using Bilateral Filtering and Depth Image-Based Rendering
Remove the blocky artifacts by cross bilateral filter
Then the depth map is used to generate 3D image by DIBR[3]
W.-Y. Chen and Y.-L. Chang and S.-F. Lin and L.-F. Ding and L.-G.
Chen, “Efficient depth image based rendering with edge dependent
depth filter and interpolation,” in Proc. ICME, pp , 2005

11. Analysis of Computational Complexity Analysis of Visual Quality
Experiment Result
Analysis of Computational Complexity
Analysis of Visual Quality

12. Analysis of Computational Complexity
The computational complexity is
Larger block size implies shorter computational time but lower depth map quality

13. Analysis of Visual Quality
Better results than the other single view based algorithm especially in vertically standing objects as in 8(b)
the images violating the initial hypothesis still generate an acceptable quality as in 8(h)
Has little side effect

14. Analysis of Visual Quality

15. Analysis of Visual Quality
Comparing the depth quality and visual comfort over 4 video data types
Videos that captured by a stereoscopic camera
Proposed algorithm
Previous work of [9]
Commercial software of DDD’s TriDef

16. Analysis of Visual Quality

17. Conclusion
The proposed algorithm uses edge information to group the image into coherent regions.
A simple depth hypothesis is determined by the linear perspective of the scene.
The algorithm is quality-scalable depending on the block size.