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

Introduction How does our brain perceive depth? Monocular cues ： one of the major categories for depth perception Motion parallax Binocular cues

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

Proposed System Block-Based Region Grouping Depth from Prior Hypothesis 3D 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 , 2005

Experiment Result Analysis of Computational Complexity Analysis 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

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

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.