Skeleton-based canonical forms for non-rigid 3D shape retrieval
Skeleton-based canonical forms for non-rigid 3D shape retrieval
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DOI：
10.1007/s41095-016-0045-5
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发表时间：
2016-04
影响因子：
6.9
通讯作者：
D. Pickup;Xianfang Sun;Paul L. Rosin;Ralph Robert Martin
中科院分区：
文献类型：
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作者：
D. Pickup;Xianfang Sun;Paul L. Rosin;Ralph Robert Martin
非刚性三维形状的检索是一个重要的任务。一种常用的技术是通过为要搜索的数据集中的每个形状生成弯曲不变的规范形式，将该问题简化为刚性形状检索任务。对于这些技术来说，通过对网格上点之间的距离应用多维缩放（MDS）来尝试“弯曲”形状是很常见的，但这会导致不必要的局部形状扭曲。我们在网格的骨架上执行不弯曲，并使用它来驱动网格本身的变形。这导致计算速度加快，并减少了局部形状细节的畸变。我们将我们的方法与其他规范形式进行比较：我们的实验表明，我们的方法在最近的规范形式基准测试中达到了最先进的检索精度，并且在第二个最近的基准测试中检索精度仅比最先进的检索精度略有下降，同时速度明显更快。
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.