Mr. Jun Wang Profile

Jun Wang

at Sichuan Highway Planning, Survey, Design, and Research Institute Ltd.

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Proceedings Article | 10 July 2024 Paper

Establishment of a 3D terrain model based on big data

Jun Wang, Guoyan Shen, Gang Luo, Yiduo Bo

Proceedings Volume 13223, 132231X (2024) https://doi.org/10.1117/12.3035660

KEYWORDS: 3D modeling, Data modeling, 3D applications, Visualization, Visual process modeling, Transportation, Design, Modeling, Point clouds, Engineering

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Currently, there is a rising tide of digitization and intelligence in the field of transportation. Concurrently, novel surveying technologies have brought about vast amounts of high-precision terrain data. However, domestic three-dimensional software development started later and progressed more slowly. The construction of three-dimensional scenes for large-scale projects such as roads, bridges, and tunnels is not yet well-developed, and it fails to fully utilize the data information brought about by surveying technologies. The difficulty of real-time roaming and rendering of large-scale terrain models is exacerbated by the abundance of data. To address this challenge, a solution is proposed based on the Delaunay triangulation. This solution centers around high-precision data and involves the implementation of surrounding elevation point supplementation, aiming to establish a high-quality, high-precision, and high-level three-dimensional terrain. On the other hand, a terrain Level of Detail (LOD) structure is established based on quadtree segmentation, enhancing model interaction calculation speed and improving rendering and roaming effects. This solution successfully constructs an efficient and reliable three-dimensional terrain model, driving the digitization and intelligence development of large-scale engineering projects. The practical application of this approach has yielded excellent results

Proceedings Article | 16 May 2024 Paper

Highway path roaming based on Gaussian filtering algorithm

Jie Yang, Jun Wang, Congkuan Zhu, Bo Wang

Proceedings Volume 13166, 131660Q (2024) https://doi.org/10.1117/12.3029095

KEYWORDS: Cameras, Gaussian filters, Tunable filters, Optical filters, Signal filtering, Visualization, Convolution, Transportation, Design, Smoothing

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The smoothing process of the roaming path coordinates and camera direction at each path point proceeds with the Gaussian filtering algorithm. The resultant connected roaming path exhibits enhanced naturalness and smoothness, mitigating the abruptness between path points. The processed virtual camera, during movement, demonstrates smoother and more fluid changes in direction, thus avoiding sudden camera shifts and reducing both visual jitter and user discomfort. Employing multithreading techniques, a dedicated timer thread ensures the consistent update of the roaming virtual camera's position and direction at a fixed frame rate. This approach diminishes the incongruent effects of virtual camera motion caused by varying frame rates and addresses inconsistencies in user input response times. The proposal enhances the temporal consistency, predictability, and physical simulation stability of the virtual camera's runtime performance. A quantitative assessment of the smoothness of the camera's turning along the path is introduced by calculating the root mean square (RMS) value of camera direction changes. This methodology is applicable for detecting abnormal paths, optimizing paths, and comparing the smoothness of camera turns along distinct routes.

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