基于节点结构的点云分类网络

Point cloud classification network based on node structure

点云数据的非结构化和不均匀分布给点云物体特征表示和分类任务带来极大挑战。为了提取点云物体的三维结构特征,现有方法多采用复杂的局部特征提取结构组建分层网络,导致特征提取网络复杂且主要关注点云物体的局部结构。为更好地提取不均匀分布的点云物体特征,提出采样点卷积密度自适应加权的节点结构网络(NsNet)。该卷积网络通过高斯密度对采样点自适应加权以区分采样点的密度差异,从而更好地刻画物体的整体结构;其次,通过加入球形坐标简化网络结构以降低模型复杂度。在3个公开数据集上与PointNet++和PointMLP等方法进行比较,实验结果表明:基于自适应密度加权的NsNet比PointNet++和PointMLP的总准确率(OA)分别提高了9.1和1.3个百分点;与PointMLP相比减少了4.6×10^(6)的参数量。NsNet可有效解决点云分布不均导致的边缘点信息损失问题,提高分类精度,降低模型复杂度。

The non-structured and non-uniform distribution of point cloud data poses significant challenges for feature representation and classification tasks.To extract the three-dimensional structural features of point cloud objects,existing methods often employ complex local feature extraction structures to construct hierarchical networks,resulting in a complex feature extraction network that mainly focuses on the local structures of the point cloud objects.To better extract features from unevenly distributed point cloud objects,a Node structure Network(NsNet)with sample point convolution density adaptive weighting was proposed.The convolutional network adaptively weighted sample points based on Gaussian density to differentiate the density differences among sampling points,thereby better characterizing the overall structure of objects.Additionally,the network structure was simplified by incorporating spherical coordinates to reduce model complexity.Experimental results on three public datasets demonstrate that,NsNet based on adaptive density weighting improves the Overall Accuracy(OA)by 9.1 and 1.3 percentage points respectively compared with PointNet++and PointMLP,and reduces the number of parameters by 4.6×10^(6) compared to PointMLP.NsNet can effectively address the problem of information loss caused by uneven distribution of point clouds,improve the classification accuracy and reduce the model complexity.