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Salient Local 3D Features for 3D Shape Retrieval

机译:3D形状检索的突出局部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,我们描述了一种基于Voxel网格的3D突出局部特征的新配方,其受到尺度不变特征变换(SIFT)的启发。我们使用它来识别3D Voxized模型上的突出关键点(不变点)并在这些关键点上计算不变的3D本地特征描述符。然后,我们在3D本地功能上使用单词方法方法来表示形状检索的3D模型。该方法的优点是它可以应用于刚性以及铰接和可变形的3D模型。最后,在McGill铰接形状基准上施加该方法的3D形状检索,然后给出并将检索结果呈现并与其他方法进行比较。

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