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首页> 外文期刊>IEEE transactions on visualization and computer graphics >Detecting 3D Points of Interest Using Multiple Features and Stacked Auto-encoder
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Detecting 3D Points of Interest Using Multiple Features and Stacked Auto-encoder

机译：使用多种功能和堆叠式自动编码器检测3D兴趣点

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Considering the fact that points of interest on 3D shapes can be discriminated from a geometric perspective, it is reasonable to map the geometric signature of a point p to a probability value encoding to what degree p is a point of interest, especially for a specific class of 3D shapes. Based on the observation, we propose a three-phase algorithm for learning and predicting points of interest on 3D shapes by using multiple feature descriptors. Our algorithm requires two separate deep neural networks (stacked auto-encoders) to accomplish the task. During the first phase, we predict the membership of the given 3D shape according to a set of geometric descriptors using a deep neural network. After that, we train the other deep neural network to predict a probability distribution defined on the surface representing the possibility of a point being a point of interest. Finally, we use a manifold clustering technique to extract a set of points of interest as the output. Experimental results show superior detection performance of the proposed method over the previous state-of-the-art approaches.
机译：考虑到可以从几何角度区分3D形状上的兴趣点这一事实，将点p的几何特征映射到编码为p的兴趣点程度的概率值是合理的，尤其是对于特定类别3D形状。基于观察，我们提出了一种三相算法，用于通过使用多个特征描述符来学习和预测3D形状上的兴趣点。我们的算法需要两个单独的深度神经网络（堆叠式自动编码器）来完成任务。在第一阶段，我们使用深度神经网络根据一组几何描述符预测给定3D形状的成员资格。之后，我们训练另一个深度神经网络来预测定义在表面上的概率分布，该概率分布表示某个点成为关注点的可能性。最后，我们使用流形聚类技术提取一组兴趣点作为输出。实验结果表明，所提出方法的检测性能优于以前的最新方法。

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