Visibility Detection of 3D Objects and Visual K-Nearest Neighbor Query Based on Convex Hull Model

Tianbao Hao, Yongshan Liu, Xiang Gong, Dehan Kong, Jianjun Wang and José García

Mathematical Problems in Engineering, 2022, vol. 2022, 1-14

Abstract: With the rapid development of the technologies of virtual reality (VR) and augmented reality (AR), the accurate visual query of 3D objects which are closer to real world has become a hotspot of study. There are a lot of redundancy problems in the existing axis-aligned bounding box model (AABB) and oriented bounding box model (OBB), which are used to represent 3D objects mostly at present, and cannot be used for the accurate visual query. For example, the results of the visual query must be accurate, and without any error in automatic driving technology in the automotive industry, otherwise, serious safety problems would be caused. The convex hull model could be used to solve those problems. However, there are few studies on visual query based on the convex hull model. A visual query method for 3D obstacle space based on the convex hull model is proposed in this paper. Firstly, the definitions of “query point-object visual body†and “combined obstacle†are proposed. Secondly, the visibility detection algorithm of the convex hull model is given in detail. The collision detection method of “query point-object visible body†combined with other obstacles and line-plane geometric calculation operations are used to detect the visibility of the convex hull model. Then, a 3D visual k-nearest neighbor algorithm based on the convex hull model is given in the paper. Finally, the algorithm is verified by experiments and compared with the traditional visibility detection algorithm, and the analysis of experimental results shows that the algorithm has a great performance and higher accuracy. The growth rate of the query time is smaller, and the speed is faster; especially, when there are fewer query points, the query speed can be increased by more than 50%.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:8302974

DOI: 10.1155/2022/8302974

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