三维点云数据实时管理的Hash map方法

本文基于机器视觉探讨数字摄影测量三维构像下的智能数据处理要素之一：海量点云高效管理技术，提出了一种基于GPU的hash map三维点云数据组织的改进算法，算法可以高效地完成数据的动态插入、更新和索引，而不受数据规模限制。同时，通过传感器位置姿态估计当前活动范围，进行主机与GPU的数据交换，保证了GPU的低内存占用率。在搭载不同等级显卡（GTX960、GTX1050、GTX1060）的计算机设备上试验，本文算法均可以达到60 fps以上的帧率（单帧处理点云数：2.11×10⁵），证明算法满足了三维构像中三维点云数据高效管理的要求。

Hash Map Method of 3D Point Cloud Data for Real-time Organizing

In this paper,one of the key elements of intelligent data processing in digital photogrammetry is discussed based on machine vision:efficient management technology of massive point cloud,an improved algorithm for hash mapping 3D data on GPU is proposed.The algorithm can efficiently perform dynamic insertion,update and indexing of data without the limitation of data scale.We applied the algorithm to TSDF (truncated signed distance field) algorithm for point cloud fusion,which can reduce the noise of single frame and the data registration error between different frames in a very efficient way.Currently,most of the data structures of point cloud fusion algorithms are regular or hierarchical grid data structures,so the object bounding boxes should be specified in advance.Moreover,the hierarchical data structure is complex and hard to parallelization.Therefore,the requirements of dynamic scalability,updating and indexing data of real-time 3D reconstruction cannot be satisfied.This paper exploited hash map to manage 3D data.At the same time,the current active region could be estimated with sensor motion to exchange data between host and GPU,keeping low memory utilization of GPU.In the experiments on different levels of graphics cards(GTX960,GTX1050,GTX1060),the algorithm can satisfy the real-time frame rate requirements(60 fps,2.11×10⁵ points per frame),so it meets the requirement of efficient management of 3D point cloud data in three-dimensional imaging.