TY - GEN
T1 - Research on the Application of Modern Exhibition Hall Visual Design Integrating VR, AR and AI Technologies
AU - Wu, Jiafeng
AU - Zeng, Zishan
AU - Wang, Yixuan
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026
Y1 - 2026
N2 - With the continuous advancement of virtual reality (VR), augmented reality (AR) and artificial intelligence (AI) technologies, the design of modern exhibition halls has gradually developed towards intelligence and interactivity. Traditional exhibition hall design methods are difficult to meet visitors’ needs for immersive experience and efficient interaction. Therefore, this study introduces convolutional neural networks for visual recognition and combines them with the A* path planning algorithm to construct a new exhibition hall visual design system, aiming to improve the display effect of exhibits in the exhibition hall and the user’s visiting experience. Experimental results show that the improved CNN-A * algorithm achieves 98.5% in path planning accuracy, which is significantly better than the A algorithm (94.2%) and the Dijkstra algorithm (92.7%). In terms of response time, the CNN-A * algorithm is 0.34 s, which is more advantageous than the A algorithm (0.45 s) and the Dijkstra algorithm (0.50 s). At the same time, CNN-A* performs well in computing resource utilization, which is only 65%, which can ensure the efficient operation of the system in complex environments. The CNN-A* algorithm shows significant advantages in path planning and interactive design of modern exhibition halls, and has good potential and application value in intelligent exhibition hall systems in terms of improving path planning accuracy, response speed and computing resource utilization.
AB - With the continuous advancement of virtual reality (VR), augmented reality (AR) and artificial intelligence (AI) technologies, the design of modern exhibition halls has gradually developed towards intelligence and interactivity. Traditional exhibition hall design methods are difficult to meet visitors’ needs for immersive experience and efficient interaction. Therefore, this study introduces convolutional neural networks for visual recognition and combines them with the A* path planning algorithm to construct a new exhibition hall visual design system, aiming to improve the display effect of exhibits in the exhibition hall and the user’s visiting experience. Experimental results show that the improved CNN-A * algorithm achieves 98.5% in path planning accuracy, which is significantly better than the A algorithm (94.2%) and the Dijkstra algorithm (92.7%). In terms of response time, the CNN-A * algorithm is 0.34 s, which is more advantageous than the A algorithm (0.45 s) and the Dijkstra algorithm (0.50 s). At the same time, CNN-A* performs well in computing resource utilization, which is only 65%, which can ensure the efficient operation of the system in complex environments. The CNN-A* algorithm shows significant advantages in path planning and interactive design of modern exhibition halls, and has good potential and application value in intelligent exhibition hall systems in terms of improving path planning accuracy, response speed and computing resource utilization.
KW - Artificial Intelligence
KW - Augmented Reality
KW - Computer Vision
KW - Interactive Design
KW - Virtual Reality
UR - https://www.scopus.com/pages/publications/105048160799
U2 - 10.1007/978-3-032-25613-3_4
DO - 10.1007/978-3-032-25613-3_4
M3 - 会议稿件
AN - SCOPUS:105048160799
SN - 9783032256126
T3 - Smart Innovation, Systems and Technologies
SP - 38
EP - 48
BT - Advances in 3D Technologies - Proceedings of 7th International Conference on 3D Imaging Technologies 2025
A2 - Kountcheva, Roumiana
A2 - Patnaik, Srikanta
A2 - Liu, Yingkai
PB - Springer Science and Business Media Deutschland GmbH
T2 - 7th International Conference on 3D Imaging Technologies-Digital Signal Processing and Deep Learning, 3DIT 2025
Y2 - 21 November 2025 through 23 November 2025
ER -