Spherical parameterization and geometry image-based 3D shape similarity estimation (CGS 2004 special issue)
Spherical parameterization and geometry image-based 3D shape similarity estimation (CGS 2004 special issue)
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DOI：
10.1007/s00371-006-0010-x
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发表时间：
2006-05
期刊：
影响因子：
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通讯作者：
Hamid Laga;H. Takahashi;M. Nakajima
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文献类型：
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作者：
Hamid Laga;H. Takahashi;M. Nakajima
在本文中，我们描述了将球面参数化和几何图像应用于 3D 形状匹配任务的初步发现。基于视图的技术通过比较 2D 投影来比较 3D 对象。然而，选择视图数量及其设置并非易事。几何图像通过将整个对象映射到球形或平面域来克服这些限制。我们利用这个属性来导出旋转不变形状描述符。一旦计算出编码对象几何属性的几何图像，就可以使用球谐分析提取一维旋转不变描述符。参数化过程保证了尺度不变性，同时其从粗到细的性质允许对不同尺度的对象进行比较。我们在 120 个三维模型的集合上展示并讨论了我们的方法的效率。
In this paper, we describe our preliminary findings in applying the spherical parameterization and geometry images to the task of 3D shape matching. View-based techniques compare 3D objects by comparing their 2D projections. However, it is not trivial to choose the number of views and their settings. Geometry images overcome these limitations by mapping the entire object onto a spherical or planar domain. We make use of this property to derive a rotation invariant shape descriptor. Once the geometry image encoding the object’s geometric properties is computed, a 1D rotation invariant descriptor is extracted using the spherical harmonic analysis. The parameterization process guarantees the scale invariance, while its coarse-to-fine nature allows the comparison of objects at different scales. We demonstrate and discuss the efficiency of our approach on a collection of 120 three-dimensional models.