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Salient Local 3D Features for 3D Shape Retrieval

机译:用于3D形状检索的显着局部3D功能

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In this paper we describe a new formulation for the 3D salient local features based on the voxel grid inspired by the Scale Invariant Feature Transform (SIFT). We use it to identify the salient keypoints (invariant points) on a 3D voxelized model and calculate invariant 3D local feature descriptors at these keypoints. We then use the bag of words approach on the 3D local features to represent the 3D models for shape retrieval. The advantages of the method are that it can be applied to rigid as well as to articulated and deformable 3D models. Finally, this approach is applied for 3D Shape Retrieval on the McGill articulated shape benchmark and then the retrieval results are presented and compared to other methods.
机译:在本文中,我们基于受比例不变特征变换(SIFT)启发的体素网格描述了一种3D显着局部特征的新公式。我们使用它来识别3D体素化模型上的显着关键点(不变点),并在这些关键点上计算不变的3D局部特征描述符。然后,我们对3D局部特征使用词袋方法来表示3D模型以进行形状检索。该方法的优点是它可以应用于刚性以及关节和可变形的3D模型。最后,将该方法应用于McGill铰接式形状基准上的3D形状检索,然后给出检索结果并将其与其他方法进行比较。

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