Free viewpoint image generation using multi-pass dynamic programming
Free viewpoint image generation using multi-pass dynamic programming
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DOI：
10.1117/12.706735
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发表时间：
2007-03
期刊：
影响因子：
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通讯作者：
Norishige Fukushima;T. Yendo;T. Fujii;M. Tanimoto
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文献类型：
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作者：
Norishige Fukushima;T. Yendo;T. Fujii;M. Tanimoto
光线空间是由基于图像的渲染（IBR）分类，因此生成的视图具有照片般的真实感质量。虽然这种方法具有高质量成像的性能，但这需要大量的图像或相机。这就是为什么光线空间需要各种方向和位置的视图，而不是3D深度信息。在本文中，我们减少了洪水的信息使用视图为中心的射线插值。以视点为中心的内插是指在生成视点时估计视点相关的深度值（或视差图），并使用多视点图像和深度信息来内插像素值的深度值。深度估计和插值的结合有效地实现了真实感图像的绘制。然而，不幸的是，如果深度估计是周或错误的，在创建图像时会出现很多伪影。因此，需要强大的深度估计方法。当我们渲染自由视点图像视频时，我们在每一帧上执行深度估计。因此，我们希望控制计算成本。我们的深度估计方法是基于动态规划（DP）。该方法在弱匹配区域优化求解深度图像，具有快速性。但由于DP的限制，扫描线噪声会出现。因此，我们执行DP多方向通道并总结多通道DP的结果。我们的方法实现了低的计算成本和高的深度估计性能。
Ray-Space is categorized by Image-Based Rendering (IBR), thus generated views have photo-realistic quality. While this method has the performance of high quality imaging, this needs a lot of images or cameras. The reason why that is Ray-Space requires various direction's and position's views instead of 3D depth information. In this paper, we reduce that flood of information using view-centered ray interpolation. View-centered interpolation means estimating view dependent depth value (or disparity map) at generating view-point and interpolating that of pixel values using multi-view images and depth information. The combination of depth estimation and interpolation realizes the rendering photo-realistic images effectively. Unfortunately, however, if depth estimation is week or mistake, a lot of artifacts appear in creating images. Thus powerful depth estimation method is required. When we render the free viewpoint images video, we perform the depth estimation at every frame. Thus we want to keep a lid on computing cost. Our depth estimation method is based on dynamic programming (DP). This method optimizes and solves depth images at the weak matching area with high-speed performance. But scan-line noises become appeared because of the limit of DP. So, we perform the DP multi-direction pass and sum-up the result of multi-passed DPs. Our method fulfills the low computation cost and high depth estimation performance.