The three-dimensional (3D) displays provide a dramatic improvement of visual quality than the 2D displays do. The
conversion of existing 2D videos to 3D videos is necessary for multimedia application. This paper presents a robust
system to convert 2D videos to 3D videos. The main concepts are to extract the depth information from motion parallax
of moving picture and to depth information from geometrical perspective in non-moving scene. In the first part, depthinduced
motion information is reconstructed by motion vector to disparity mapping. By warping the consecutive video
frames to parallel view angle with the current frame, the frame with suitable baseline is selected to generate depth using
motion parallax information. However, video may not have the depth-induced motion information in every case. For
scene without motion parallax, depth from geometrical perspective is applied to generate scene depth map. Scene depth
map is assigned depending on the scene mode and analyzed line structure in the video. Combining these two depth cues,
the stereo effect is enhanced and provide spectacular depth map. The depth map is then used to render the multi-view
video for 3D display.
For the sake of providing 3D contents for up-coming 3D display devices, a real-time automatic depth fusion
2D-to-3D conversion system is needed on the home multimedia platform. We proposed a priority depth fusion
algorithm with a 2D-to-3D conversion system which generates the depth map from most of the commercial video
sequences. The results from different kinds of depth reconstruction methods are integrated into one depth map
by the proposed priority depth fusion algorithm. Then the depth map and the original 2D image are converted
to stereo images for showing on the 3D display devices. In this paper, a 2D-to-3D conversion algorithm set
is combined with the proposed depth fusion algorithm to show the improved results. With the converted 3D
contents, the needs for 3D display devices will also increase. As long as the two technologies evolve, the 3D-TV
era will come as soon as possible.
This paper presented a novel dense disparity estimation method which is called as symmetric trinocular dense
disparity estimation. Also a car surrounding camera array application is proposed to improve the driving safety
by the proposed symmetric trinocular dense disparity estimation algorithm. The symmetric trinocular property
is conducted to show the benefit of doing disparity estimation with three cameras. A 1D fast search algorithm is
described to speed up the slowness of the original full search algorithms. And the 1D fast search algorithm utilizes
the horizontal displacement property of the cameras to further check the correctness of the disparity vector. The
experimental results show that the symmetric trinocular property improves the quality and smoothness of the
disparity vector.
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