SDG Cut: 3D Reconstruction of Non-lambertian Objects Using Graph Cuts on Surface Distance Grid
SDG Cut: 3D Reconstruction of Non-lambertian Objects Using Graph Cuts on Surface Distance Grid
复制标题
SDG Cut：使用表面距离网格上的图形切割对非朗伯对象进行 3D 重建
DOI：
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
Wei
中科院分区：
文献类型：
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作者：
Tianli Yu;N. Ahuja;Wei
我们发现，使用体积图切割来最小化物体表面上的代价函数的三维重建方法有两种类型的偏差：最小表面偏差和离散化偏差。这些偏差使得很难恢复曲面挤出和其他细节，特别是在使用非朗伯照片一致性测量时。为了减少这些偏差，我们提出了一种新的基于迭代图割的算法，该算法运行在曲面距离网格(SDG)上，SDG是三维空间的一种特殊离散化，使用当前曲面估计的符号距离变换来构造。结果表明，SDG显著降低了表面的最小偏差，并将离散化偏差转化为可控的表面光滑度。通过对非朗伯物体的三维重建实验，验证了该算法的有效性。
We show that the approaches to 3D reconstruction that use volumetric graph cuts to minimize a cost function over the object surface have two types of biases, the minimal surface bias and the discretization bias. These biases make it difficult to recover surface extrusions and other details, especially when a non-lambertian photo-consistency measure is used. To reduce these biases, we propose a new iterative graph cuts based algorithm that operates on the Surface Distance Grid (SDG), which is a special discretization of the 3Dspace, constructed using a signed distance transform of the current surface estimate. It can be shown that SDG significantly reduces the minimal surface bias, and transforms the discretization bias into a controllable degree of surface smoothness. Experiments on 3D reconstruction of non-lambertian objects confirm the effectiveness of our algorithm over previous methods.