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
Jan 04, 2016
A Novel 2D-to-3D Conversion System Using
Edge Information
IEEE Transactions on Consumer Electronics 2010Chao-Chung Cheng
Chung-Te li
Liang-Gee Chen
Introduction
Some approaches that can generate 3D contentTime-of-flight depth sensorTriangular stereo vision3D graph rendering
Introduction
How does our brain perceive depth?Monocular cues: one of the major categories for depth
perceptionMotion parallax
Binocular cues
Monocular cues
Interposition (overlapping)
Relative Height
Familiar Size
Texture Gradient
Shadow
Linear Perspective
Proposed System
Block-Based Region GroupingDepth from Prior Hypothesis3D Image Visualization using Bilateral
Filtering and Depth Image-Based Rendering
Proposed 2D-to-3D Conversion System
Block-Based Region Grouping
1. Measure the similarity of neighboring blocks
2. The blocks are segmented into multiple groups by MST
Depth from Prior Hypothesis
1. 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
Depth from Prior Hypothesis
2. Find the corresponding depth map gradients
3. Compute the gravity center of the block group as the depth
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. 1314-1317, 2005
Experiment Result
Analysis of Computational ComplexityAnalysis of Visual Quality
Analysis of Computational Complexity
The computational complexity is Larger block size implies shorter computational
time but lower depth map quality
Analysis of Visual Quality
Analysis of Visual Quality
Analysis of Visual Quality
Comparing the depth quality and visual comfort over 4 video data typesVideos that captured by a stereoscopic cameraProposed algorithmPrevious work of [9]Commercial software of DDD’s TriDef
Analysis of Visual Quality
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.