三维模型普适性特征提取与分类

Universal Feature Extraction and Classification for 3D Models

为了解决单一算法特征描述子难以兼顾表达刚性和非刚性三维模型的问题,提出一种三维模型普适性特征提取方法.首先提出一种基于三维点云模型的局部面积加权密集化采样算法;然后针对非刚性铰链结构的变换影响,利用热核特征的等距等容不变性提出时间尺度序列热核编码方法;最后提出边缘投影图卷积神经网络,对编码点云的空间形状及时间尺度序列热核进行特征融合学习,并应用于三维模型分类任务.在刚性三维模型数据集ModelNet40和非刚性三维模型数据集SHREC15上的实验结果表明,与单一刚性或非刚性三维模型特征提取方法相比,所提方法能够提取具有普适性且具有显著辨别力的特征描述符,分类准确率分别达到92.63%和97.71%.

In order to solve the problem that a single algorithm feature descriptor is difficult to express both rigid and non-rigid 3D models,this paper propose a universal feature extraction method for 3D models.Firstly,a local area weighted densification sampling method based on 3D point cloud model is proposed.Secondly,aim at the influence of non-rigid hinge structure transformation,a time-scale sequence heat kernel encoding method is proposed by using the equidistant and isometric invariance of heat kernel signature.Finally,an edge-projection graph convolutional neural network which is applied to 3D model classification tasks is designed.This network learns the feature fusion on the spatial shape of the encoded point cloud and the time-scale sequence heat kernel.Experiments on the rigid 3D model dataset ModelNet40 and non-rigid 3D model dataset SHREC15 show that compared with the single rigid or non-rigid 3D model feature extraction methods,the proposed method can extract universal feature descriptors with significant discrimination,and the classification accuracy rates at 92.63% and 97.71%,respectively.