Faculty of Engineering, Bar-Ilan University, Ramat Gan 52900, Israel.
IEEE Trans Image Process. 2012 May;21(5):2758-69. doi: 10.1109/TIP.2012.2183142. Epub 2012 Jan 9.
In this paper, we present a framework for detecting interest points in 3-D meshes and computing their corresponding descriptors. For that, we propose an intrinsic scale detection scheme per interest point and utilize it to derive two scale-invariant local features for mesh models. First, we present the scale-invariant spin image local descriptor that is a scale-invariant formulation of the spin image descriptor. Second, we adapt the scale-invariant feature transform feature to mesh data by representing the vicinity of each interest point as a depth map and estimating its dominant angle using the principal component analysis to achieve rotation invariance. The proposed features were experimentally shown to be robust to scale changes and partial mesh matching, and they were compared favorably with other local mesh features on the SHREC'10 and SHREC'11 testbeds. We applied the proposed local features to mesh retrieval using the bag-of-features approach and achieved state-of-the-art retrieval accuracy. Last, we applied the proposed local features to register models to scanned depth scenes and achieved high registration accuracy.
在本文中，我们提出了一种用于检测 3D 网格中的兴趣点并计算其相应描述符的框架。为此，我们针对每个兴趣点提出了一种内在尺度检测方案，并利用该方案为网格模型推导出两个尺度不变的局部特征。首先，我们提出了尺度不变的旋转图像局部描述符，这是旋转图像描述符的尺度不变形式。其次，我们通过将每个兴趣点的邻域表示为深度图，并使用主成分分析估计其主导角度来实现旋转不变性，从而将尺度不变特征变换特征适配到网格数据中。实验表明，所提出的特征对尺度变化和部分网格匹配具有鲁棒性，并且在 SHREC'10 和 SHREC'11 测试平台上与其他局部网格特征相比具有优势。我们使用基于特征的方法将所提出的局部特征应用于网格检索，并实现了最先进的检索精度。最后，我们将所提出的局部特征应用于模型到扫描深度场景的配准，并实现了高精度的配准。
