A 3D roaming and collision detection algorithm applicable for massive spatial data

RESEARCH ARTICLE A 3D roaming and collision detection algorithm applicable for massive spatial data Mingxia Xie ID1,2*, Xinqiang Niu2 1 Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources, Shenzhen, China, 2 Changjiang Institute of Survey, Planning, Design and Research, Wuhan, China * a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Xie M, Niu X (2020) A 3D roaming and collision detection algorithm applicable for massive spatial data. PLoS ONE 15(2): e0229038. https:// doi.org/10.1371/journal.pone.0229038 Editor: Yong Peng, Central South University, CHINA Received: August 27, 2019 Accepted: January 28, 2020 Published: February 21, 2020 Copyright: © 2020 Xie, Niu. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the paper and its Supporting Information files. Funding: This work was supported by the Project Supported by the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources (KF-2018-03-050), and China Postdoctoral Science Foundation (2018M642800), National Key R&D Program of China(2017YFC1502600). Competing interests: The authors have declared that no competing interests exist. Abstract In this paper, a novel 3D roaming algorithm considering collision detection and interaction is proposed that adopts a triangle mesh to organize and manage massive spatial data and uses a customized bounding box intersector to rapidly obtain the potential collided triangles. The proposed algorithm can satisfy the requirements of timeliness and practicability during complicated large 3D scene collision detection. Moreover, we designed a method to calculate the collision point coordinates according to the spatial position relation and distance change between the virtual collision detection sphere and triangles, with the triangle edges and three vertices being considered. Compared to the methods that use the native intersector of OpenSceneGraph (OSG) to obtain the collision point coordinates, the calculation efficiency of the proposed method is greatly improved. Usually, when there is a big split/pit in the scene, the viewpoints will fly off the scene due to the fall of the collision detection sphere, or the region interior cannot be accessed when the entrance of some local region (e.g., internal grotto) of the scene is too small. These problems are solved in this paper through 3D scene-path training and by self-adaptively adjusting the radius of the virtual collision detection sphere. The proposed 3D roaming and collision detection method applicable for massive spatial data overcomes the limitation that the existing roaming and collision detection methods are only applicable to 3D scenes with a small amount of data and simple models. It provides technical supports for freewill browsing and roaming of indoor/outdoor and overground/underground of the 3D scene in cases of massive spatial data. Introduction With the rapid development of 3D spatial data acquisition technologies (e.g., 3D laser scanner, oblique photography, UAV measurement, and BIM) as well as spatial object modelling technologies, the availability of 3D spatial data with various scales or different degrees of details has been greatly improved. The data size and complexity of 3D scenes have sharply increased while critically improving the verisimilitude of 3D scenes. Presently, 3D spatial data acquisition and modelling technologies have been widely applied to digital twins and the product life-cycle management of engineering (i.e., water resource and hydropower engineering, constructional engineering, underground pipe network engineering, and railroad and bridge PLOS ONE | https://doi.org/10.1371/journal.pone.0229038 February 21, 2020 1 / 15 A 3D roaming and collision detection algorithm engineering). During the application process, the constructed 3D scene is both a 3D geospatial environment model with a macro-region and a complicated 3D virtual scene with a micro and exquisite building model. When the viewing point enters from the macroscopic scene into the micro indoor scene, if it does not consider the collision detection problem and roams and analyzes indoor, it will lead to the problem of incorrect analysis and calculation due to the inaccurate capture or acquisition of the viewing point. Moreover, during the roaming process, it has the phenomena of penetration (e.g., flying into the ground and passing through the wall) that do not conform with real situations, which makes it difficult to roam and analyze indoors or makes it unable to operate. Scene roaming and collision detection are the core technologies of virtual scenes. Their effects directly influence the reality of the whole virtual scene [1]. The existing roaming algorithms considering collision detection mainly construct the target bounding box to judge the collision relation among actual objects and find the balance between the close joint degree of bounding box with the actual collision object surface with the collision detection effectiveness; thus, roaming in 3D scenes is realized [2][3]. Existing algorithms have mainly been applied to 3D scenes with less data volume and to simple models. There are still no clear and effective solutions regarding how to realize high-efficient roaming in consideration of real-time collision detection and interaction in massive and complicated 3D scenes so as to effectively avoid the phenomenon of penetration that does not conform to actual situations. Besides, the problem of inaccurate capturing of the viewing point remains. However, it has critical significance in the fields of digital twins and engineering product life-cycle management for integrated management, analysis, roaming, and applications of massive indoor/outdoor and overground/ underground spatial data. In addition to improving the sense of reality for virtual scenes, it is convenient for users’ indoor operations, such as searching and analyzing. Current situation analysis In a 3D scene, people usually observe an object with their eyes. If the viewing point moves, the surrounding scenery moves reversely relative to the viewing point. This process leads to a scene roaming effect in the brain. Collision is caused by user interaction and object movement during roaming [4]. During scene roaming, when the system receives the user input, it needs to judge if the new viewing points corresponding to the user input will collide with or enter objects in the scene [5] [6]. Analysis of collision detection methods There are two types of collision detection methods: intersector detection methods and bounding box detection methods. In intersector detection methods, the iterator is bonded with (...truncated)