The Marching Cubes algorithm (MC) is a powerful surface rendering technique which can produce very high quality images. However, it is not suitable for interactive manipulation of the 3D surfaces constructed from high resolution volume data sets in terms of both space and time. In this paper, we present an adaptive version of MC called Adaptive Marching Cubes (AMC). It significantly reduces the number of triangles representing the surface by adapting the size of the triangles to the shape of the surface. This improves the performance of the manipulation of the 3D surfaces.A typical example with the volume data set of size shows that the number of triangles is reduced by 55%. The quality of images produced by AMC is similar to that of MC. One of the fundamental problems encountered with adaptive algorithms is the crack problem. Cracks may be created between two neighboring cubes processed with different levels of subdivision. We solve the crack problem by patching the cracks using polygons of the same shape as those of the cracks. We propose a simple but complete method by first abstracting 22 basic configurations of arbitrary sized cracks and then reducing the handling of these configurations to a simple rule. It requires only O(n 2 ) working memory for a volume data set.

With the recent booming of 3DTV industry, more and more stereoscopic videos are demanded by the market. This paper presents a system of converting conventional monocular videos to stereoscopic ones. In this system, an input video is firstly segmented into shots to reduce operations on similar frames. Then, automatic depth estimation and interactive image segmentation are integrated to obtain depth maps and foreground/background segments on selected key frames. Within each video shot, such results are propagated from key frames to non-key frames. Combined with a depthto-disparity conversion method, the system synthesizes the counterpart (either left or right) view for stereoscopic display by warping the original frame according to disparity maps. For evaluation, we use human labeled depth map as the reference and compute both the mean opinion score (MOS) and Peak signal-to-noise ratio (PSNR) to evaluate the converted video quality. Experiment results demonstrate that the proposed conversion system and methods achieves encouraging performance.

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Abstract-This paper presents a system of converting conventional monocular videos to stereoscopic ones. In the system, an input monocular video is firstly segmented into shots so as to reduce operations on similar frames. An automatic depth estimation method is proposed to compute the depth maps of the video frames utilizing three monocular depth cues -depth-fromdefocus, aerial perspective and motion. Foreground/background objects can be interactively segmented on selected key frames and their depth values can be adjusted by users. Such results are propagated from key frames to non-key frames within each video shot. Equipped with a depth-to-disparity conversion module, the system synthesizes the counterpart (either left or right) view for stereoscopic display by warping the original frames according to their disparity maps. The quality of converted videos is evaluated by human mean opinion scores (MOS), and experiment results demonstrate that the proposed conversion method achieves encouraging performance.Index Terms-2D-to-3D (stereoscopic) video conversion, depth estimation, stereoscopic video quality evaluation.