Zeng Sheng, Geng Guohua, Zhou Mingquan
School of Information Science & Technology, Northwest University, Xi'an 710127, China.
Entropy (Basel). 2021 Nov 23;23(12):1561. doi: 10.3390/e23121561.
Automatically selecting a set of representative views of a 3D virtual cultural relic is crucial for constructing wisdom museums. There is no consensus regarding the definition of a good view in computer graphics; the same is true of multiple views. View-based methods play an important role in the field of 3D shape retrieval and classification. However, it is still difficult to select views that not only conform to subjective human preferences but also have a good feature description. In this study, we define two novel measures based on information entropy, named depth variation entropy and depth distribution entropy. These measures were used to determine the amount of information about the depth swings and different depth quantities of each view. Firstly, a canonical pose 3D cultural relic was generated using principal component analysis. A set of depth maps obtained by orthographic cameras was then captured on the dense vertices of a geodesic unit-sphere by subdividing the regular unit-octahedron. Afterwards, the two measures were calculated separately on the depth maps gained from the vertices and the results on each one-eighth sphere form a group. The views with maximum entropy of depth variation and depth distribution were selected, and further scattered viewpoints were selected. Finally, the threshold word histogram derived from the vector quantization of salient local descriptors on the selected depth maps represented the 3D cultural relic. The viewpoints obtained by the proposed method coincided with an arbitrary pose of the 3D model. The latter eliminated the steps of manually adjusting the model's pose and provided acceptable display views for people. In addition, it was verified on several datasets that the proposed method, which uses the Bag-of-Words mechanism and a deep convolution neural network, also has good performance regarding retrieval and classification when dealing with only four views.
自动选择一组3D虚拟文物的代表性视图对于构建智慧博物馆至关重要。在计算机图形学中，对于好视图的定义尚无共识；多个视图的情况也是如此。基于视图的方法在3D形状检索和分类领域发挥着重要作用。然而，仍然难以选择既符合人类主观偏好又具有良好特征描述的视图。在本研究中，我们基于信息熵定义了两种新的度量，即深度变化熵和深度分布熵。这些度量用于确定每个视图关于深度摆动和不同深度量的信息量。首先，使用主成分分析生成一个规范姿态的3D文物。然后，通过细分规则的单位八面体，在测地单位球体的密集顶点上捕获由正交相机获得的一组深度图。之后，分别在从顶点获得的深度图上计算这两种度量，并且每个八分之一球体上的结果形成一组。选择深度变化熵和深度分布熵最大的视图，并进一步选择分散的视点。最后，从所选深度图上显著局部描述符的矢量量化导出的阈值词直方图表示3D文物。所提出的方法获得的视点与3D模型的任意姿态一致。后者省去了手动调整模型姿态的步骤，并为人们提供了可接受的显示视图。此外，在几个数据集上验证了所提出的方法，该方法使用词袋机制和深度卷积神经网络，在仅处理四个视图时在检索和分类方面也具有良好的性能。
