Xianglong Li
Research Subjects
Xianglong Li has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Chenliang Wang
School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou 325000, China
1 shared publication - Guoxiong Chen
SuperMap Software Co., Ltd., Beijing 100015, China
1 shared publication - Kejia Huang
SuperMap Software Co., Ltd., Beijing 100015, China
1 shared publication - Runying Liu
SuperMap Software Co., Ltd., Beijing 100015, China
1 shared publication - Shaohua Wang
State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
1 shared publication
Latest Publications
- Open Access
- 8 September 2021
- Published by MDPI AG in ISPRS International Journal of Geo-Information
- Vol. 10 (9) , 593
An Efficient, Platform-Independent Map Rendering Framework for Mobile Augmented Reality
With the extensive application of big spatial data and the emergence of spatial computing, augmented reality (AR) map rendering has attracted significant attention. A common issue in existing solutions is that AR-GIS systems rely on different platform-specific graphics libraries on different operating systems, and rendering implementations can vary across various platforms. This causes performance degradation and rendering styles that are not consistent across environments. However, high-performance rendering consistency across devices is critical in AR-GIS, especially for edge collaborative computing. In this paper, we present a high-performance, platform-independent AR-GIS rendering engine; the augmented reality universal graphics library (AUGL) engine. A unified cross-platform interface is proposed to preserve AR-GIS rendering style consistency across platforms. High-performance AR-GIS map symbol drawing models are defined and implemented based on a unified algorithm interface. We also develop a pre-caching strategy, optimized spatial-index querying, and a GPU-accelerated vector drawing algorithm that minimizes IO latency throughout the rendering process. Comparisons to existing AR-GIS visualization engines indicate that the performance of the AUGL engine is two times higher than that of the AR-GIS rendering engine on the Android, iOS, and Vuforia platforms. The drawing efficiency for vector polygons is improved significantly. The rendering performance is more than three times better than the average performances of existing Android and iOS systems.
- 8 September 2021
- Published by MDPI AG in ISPRS International Journal of Geo-Information
- Vol. 10 (9) , 593
Open AccessAn Efficient, Platform-Independent Map Rendering Framework for Mobile Augmented Reality
With the extensive application of big spatial data and the emergence of spatial computing, augmented reality (AR) map rendering has attracted significant attention. A common issue in existing solutions is that AR-GIS systems rely on different platform-specific graphics libraries on different operating systems, and rendering implementations can vary across various platforms. This causes performance degradation and rendering styles that are not consistent across environments. However, high-performance rendering consistency across devices is critical in AR-GIS, especially for edge collaborative computing. In this paper, we present a high-performance, platform-independent AR-GIS rendering engine; the augmented reality universal graphics library (AUGL) engine. A unified cross-platform interface is proposed to preserve AR-GIS rendering style consistency across platforms. High-performance AR-GIS map symbol drawing models are defined and implemented based on a unified algorithm interface. We also develop a pre-caching strategy, optimized spatial-index querying, and a GPU-accelerated vector drawing algorithm that minimizes IO latency throughout the rendering process. Comparisons to existing AR-GIS visualization engines indicate that the performance of the AUGL engine is two times higher than that of the AR-GIS rendering engine on the Android, iOS, and Vuforia platforms. The drawing efficiency for vector polygons is improved significantly. The rendering performance is more than three times better than the average performances of existing Android and iOS systems.