Choose your own viewpoint: A high-quality/low-complexity free-viewpoint 3D visual system

Choosing one's own viewpoint when watching a video program has long been a desire for viewers. To achieve this goal, view synthesis and depth map generation are two fundamental techniques. View synthesis is a signal processing procedure which creates dense virtual views based on sparse real views. Each object inside a frame is warped to a proper position according to its depth information to form the viewpoint changing perception for viewers. Hence, the correctness of depth map influences the view synthesis quality. To increase the accuracy of depth map, this paper proposes an edge-adaptive block matching scheme cooperated with an unreliable region repairing approach. The former avoid finding local minimum in stereo matching, and the latter repairs the errors caused by occlusion regions. As for view synthesis, this paper proposes a special warping method that can detect errors caused by boundary mismatches of objects between corresponding depth and color images to improve quality of the synthesized view. Besides, we also propose a compensative-filling method that can fix tiny cracks due to round-off errors. Because of these two features, the proposed view synthesis becomes more robust to tolerate errors inside depth maps when compared with previous schemes. Both the depth generation and view synthesis are extremely complex computations. Therefore, this paper also proposes a low-complexity computing technology based on group-of-pixels which increases 30 times of performance for depth map generation, and reduces 60% computation time of view synthesis.

[1]  Jun Zhang,et al.  Depth-level-adaptive view synthesis for 3D video , 2010, 2010 IEEE International Conference on Multimedia and Expo.

[2]  Hsu-Feng Hsiao,et al.  A depth refinement algorithm for multi-view video synthesis , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[3]  Toshiaki Fujii,et al.  Error supression in view synthesis using reliability reasoning for FTV , 2010, 2010 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video.

[4]  Gauthier Lafruit,et al.  Cross-Based Local Stereo Matching Using Orthogonal Integral Images , 2009, IEEE Transactions on Circuits and Systems for Video Technology.

[5]  Sehoon Yea,et al.  Occlusion handling based on support and decision , 2010, 2010 IEEE International Conference on Image Processing.

[6]  Tian-Sheuan Chang,et al.  Algorithm and Architecture of Disparity Estimation With Mini-Census Adaptive Support Weight , 2010, IEEE Transactions on Circuits and Systems for Video Technology.

[7]  Gauthier Lafruit,et al.  Stream-Centric Stereo Matching and View Synthesis: A High-Speed Approach on GPUs , 2009, IEEE Transactions on Circuits and Systems for Video Technology.

[8]  Jae Wook Jeon,et al.  FPGA Design and Implementation of a Real-Time Stereo Vision System , 2010, IEEE Transactions on Circuits and Systems for Video Technology.

[9]  Katsushi Ikeuchi,et al.  Disparity map refinement and 3D surface smoothing via Directed Anisotropic Diffusion , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[10]  D. Scharstein,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001).

[11]  Dongxiao Li,et al.  Asymmetric bidirectional view synthesis for free viewpoint and three-dimensional video , 2009, IEEE Transactions on Consumer Electronics.