利用模型相似性的三维模型簇协同分割

Co-segmentation of three-dimensional shape clusters by shape similarity

为准确捕捉三维点云模型的上下文信息,提高分割准确率,提出一种利用模型相似性进行三维模型簇协同分割的方法。首先,对点云模型进行最远点采样得到质心点,并采用球内随机选取的方式确定邻域点以构建球形邻域;然后,使用特征聚合算子编码三维点云之间的几何拓扑关系,提取各邻域间的相关联特征,并利用各球形邻域的质心坐标构建空间相似性矩阵,由空间相似性矩阵对编码器网络所提取的模型局部特征进行加权求和,完成对三维模型的协同分析;最后,搭建分层特征提取网络对经过加权处理的关联特征进行解码操作,完成模型簇协同分割任务。实验结果表明,本文算法在ShapeNet Part数据集上的协同分割准确率达到了86.0%。与kNN算法相比,以球内随机选取法为邻域点采样策略,可使网络的分割准确率提升0.8%;相比于使用共享的多层感知机进行特征提取,使用特征聚合算子进行卷积运算可使网络的分割准确率提高12.9%;与当前主流的模型分割算法相比,本文算法的分割准确率得到了进一步的提升。

To accurately capture the context information of three-dimensional(3D)point cloud shapes and improve the accuracy of segmentation,we propose a method for the co-segmentation of 3D shape clusters using shape similarity.First,a Farthest Point Sampling is performed on the point cloud shape to obtain the centroid point,and a random pick method is used to determine the neighborhood points to construct a spherical neighborhood.Then,the feature aggregation operator is used to encode geometric topological relationships of 3D point cloud.The associated features among the neighborhood is extracted,and a spatial similarity matrix is constructed using the centroid coordinates of each spherical neighborhood.The spatial similarity matrix sums the weighted local features of the shape extracted by the encoder network to complete the collaborative analysis of the 3D shape.Finally,a hierarchical feature extraction network is built to decode the weighted associated features and complete the shape cluster co-segmentation task.Experimental results show that the co-segmentation accuracy of our algorithm on the ShapeNet Part dataset reaches 86.0%.Compared to the k-nearest neighbor algorithm,using the random selection method within a sphere as the neighborhood point sampling strategy can increase the segmentation accuracy of the network by 1.5%.Compared to the use of shared multilayer perceptrons for feature extraction,the use of feature aggregation operators for convolution operations can increase the segmentation accuracy of the network by 5.6%.Moreover,compared to the current mainstream shape segmentation algorithms,the segmentation accuracy of the proposed algorithm is superior.