【摘要】 点云提供了精确的空间位置信息而被广泛应用于环境感知领域。近年来，越来越多的工作尝试直接以点云作为输入进行特征提取，Pointnet[10]和Pointnet++[11]是这个方向的开创者，但Pointnet++没有考虑点云非均匀采样的问题。研究提出了DST-Pointnet++对其进行改进，通过核密度估计和非线性变换从点云中提取出逆密度因子，将其与原点云特征进行加权，得到了具有密度信息的点云特征，提高了边缘点对局部特征的贡献，改善了因点云分布不均造成的问题。经过在公开数据集上测试对比，结果表明DST-Pointnet++具有更好的准确率和鲁棒性。更多还原

【Abstract】 Point cloud provides accurate spatial location information and it is widely used in environmental perception area.More and more work attempts to extract features directly from point clouds in recent years,Pointnet[10]and Pointnet++[11]are the pioneers in this direction,but Pointnet++ does not consider non-uniform sampling. The research proposes DST-Pointnet++ to improve it. The paper extractes the inverse density factor from point cloud through kernel density estimation and nonlinear transformation,then weightes it with the original point cloud feature to obtain a new feature with density information. DST-Pointnet++ increases the contribution of edge points to local features and improves the problem caused by points cloud non-uniform distribution.After testing and comparison on public data sets,the results show that DST-Pointnet++ has better accuracy and robustness.更多还原