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

doi:10.11947/j.AGCS.2018.20170619

Abstract

Abstract: 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.

Key words: 3D point cloud, data organization, hash table, graphics processing unit (GPU), parallel computing

CLC Number:

P234

ZHENG Shunyi, HE Yuan, XU Gang, WANG Chen, ZHU Fengbo. Hash Map Method of 3D Point Cloud Data for Real-time Organizing[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(6): 825-832.

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