Detecting 3D Points of Interest Using Multiple Features and Stacked Auto-encoder
Detecting 3D Points of Interest Using Multiple Features and Stacked Auto-encoder
复制标题
使用多个特征和堆叠自动编码器检测 3D 兴趣点
DOI：
10.1109/tvcg.2018.2848628
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发表时间：
2019-08-01
影响因子：
5.2
通讯作者：
Kavan, Ladislav
中科院分区：
文献类型：
--
作者：
Shu, Zhenyu;Xin, Shiqing;Kavan, Ladislav
考虑到3D形状上的兴趣点可以从几何角度区分的事实，将点$p$p的几何签名映射到编码$p$p是兴趣点的程度的概率值是合理的，特别是对于特定类别的3D形状。基于观察，我们提出了一个三阶段的算法学习和预测点的兴趣在三维形状，通过使用多个特征描述符。我们的算法需要两个独立的深度神经网络（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在表面上定义的概率分布，该概率分布表示点是感兴趣点的可能性。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，该方法的检测性能优于以前的国家的最先进的方法上级。
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 $p$p to a probability value encoding to what degree $p$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.