PCU-Net:基于改进PCN的点云补全均匀算法

PCU-Net:Point Cloud Completion Dense Algorithm Based on Improved PCN

激光雷达采集的点云数据往往是稀疏且无序的。对于需要通过处理三维点云的研究来说,直接处理激光雷达采集的数据容易产生差错,需要对采集的点云进行预处理。现有算法致力于恢复点云的拓扑结构,忽略了稀疏的点云容易丢失特征信息。针对上述问题,提出了一个神经网络,可以将原来残缺、稀疏的点云生成为完整、密集、均匀的点云,称为点云补全均匀化网络(Points Completion Uniform Net,PCU-Net)。该网络基于点云补全网络(Point Completion Network,PCN)引入了一种能够快速提取全局特征的轻量化结构,并在解码器中补全和稠密化点云。还提出了一种精炼器模块,从输入中保留原始细节,通过最远点采样(Farthest Point Sampling,FPS)和点特征残差网络均匀化点云。在开源数据集Visionair上通过实验对比,该算法在点云补全上较目前主流补全算法有所提升,并在点云均匀化和稠密化上取得良好的效果。

The point cloud data collected by lidar is often sparse and disordered.For the research that needs to process three-dimensional point clouds,it is to make mistakes by directly processing the data collected by lidar,so it is necessary to preprocess the collected point clouds.The existing algorithms focus on restoring the topology of point clouds,while ignoring that sparse point clouds are prone to lose feature information.To solve the above problems,a neural network is proposed,which can generate a complete,dense and uniform point cloud which called Points Completion Uniform Net(PCU-Net)by using the original incomplete and sparse point cloud.In this network,based on Point Completion Network(PCN),a lightweight structure is introduced to extract global features quickly and complete and densify the point cloud in the decoder.In addition,a refiner module is proposed,which preserves the original details from the input and unifies the point cloud through the farthest point sampling and the point feature residual network.Through experimental comparison on Visionair open source data set,the point cloud completion is improved by the proposed algorithm compared with the current mainstream completion algorithm,and the proposed algorithm can achieve good results in point cloud homogenization and densification.