PointMLP-FD:基于多级自适应下采样的点云分类模型

PointMLP-FD: a point cloud classification model based on multi-level adaptive downsampling

针对受硬件条件、物体遮挡和背景杂波等客观因素的影响,传感器采集的目标点云具有较强的稀疏性和密度不均匀性,导致分类模型对点云特征的学习效率低、分类泛化能力差的问题,提出了一种基于多级自适应下采样的点云分类模型PointMLP-FD。该模型设计了多个MLP模块作为网络分支,以点云的浅层特征为输入得到每个点云类别维度上的特征表达,之后再根据特征表达进行排序,选择具有更强语义特征的点构成下采样点集。通过过滤背景和与目标相关性低的信息来自适应保留反应目标本质特征的信息。最后分别计算分支网络的损失,与骨干网络并行训练来优化点云特征,减少模型参数。该方法在ScanObjectNN数据集上进行测试,结果表明相较于PointMLP-elite分类精度更高,mAcc提升1%,OA提升0.8%,以更少的参数量接近SOTA模型的性能。

Due to the influence of objective factors, such as hardware limitations, object occlusion, and background clutter, the target point clouds collected by sensors have strong sparsity and density inhomogeneity, resulting in low learning efficiency of point cloud features by the classification model and poor classification generalization ability. To address these challenges, a point cloud classification model PointMLP-FD(feature-driven) was proposed based on multi-level adaptive downsampling. Multiple MLP modules were designed as network branches in the model, and with the shallow features of point clouds as inputs, feature expressions in each point cloud category dimension could be obtained. Then the points with stronger semantic features were selected to form the downsampled point set according to the ranking of the feature expressions. The information reflecting the essential features of the target could be self-adaptively retained by filtering the background and the information with low relevance to the target. Finally,the losses of branch networks were calculated separately and trained in parallel with the backbone network to optimize the point cloud features and reduce the model parameters. The proposed method was tested on the ScanObjectNN dataset, and the results show that compared with PointMLP-elite, the classification accuracy is higher, with 1%improvement in mAcc and 0.8% improvement in OA, approaching the performance of the SOTA model with fewer parameters.