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A Novel 2D-to-3D Conversion System Using Edge Information

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

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A Novel 2D-to-3D Conversion System Using Edge Information

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  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

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

  5. 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. 1314-1317, 2005

  11. 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

  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.

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