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Skeleton-based canonical forms for non-rigid 3D shape retrieval

机译:用于非刚性3D形状检索的基于骨架的规范形式

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Abstract The retrieval of non-rigid 3D shapes is an important task. A common technique is to simplify this problem to a rigid shape retrieval task by producing a bending-invariant canonical form for each shape in the dataset to be searched. It is common for these techniques to attempt to “unbend” a shape by applying multidimensional scaling (MDS) to the distances between points on the mesh, but this leads to unwanted local shape distortions. We instead perform the unbending on the skeleton of the mesh, and use this to drive the deformation of the mesh itself. This leads to computational speed-up, and reduced distortion of local shape detail. We compare our method against other canonical forms: our experiments show that our method achieves state-of-the-art retrieval accuracy in a recent canonical forms benchmark, and only a small drop in retrieval accuracy over the state-of-the-art in a second recent benchmark, while being significantly faster.
机译:摘要非刚性3D形状的检索是一项重要任务。一种常见的技术是通过为要搜索的数据集中的每个形状生成不变的规范形式来将这个问题简化为刚性形状检索任务。这些技术通常尝试通过对网格上各点之间的距离应用多维缩放(MDS)来“解除弯曲”形状,但这会导致不希望的局部形状变形。取而代之的是,我们对网格的骨架进行不弯曲,并使用它来驱动网格本身的变形。这样可以提高计算速度,并减少局部形状细节的失真。我们将我们的方法与其他规范形式进行了比较:我们的实验表明,我们的方法在最近的规范形式基准中达到了最新的检索精度,而检索精度仅比最新规范形式中的检索精度下降了一点最近第二次基准测试,同时速度明显提高。

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