An Augmented Reality System for Presenting Architecture Models in Outdoor Scenes

Authors

  • Jhan-Wei Lin Department of Computer Science and Information Engineering, National United University, Taiwan
  • Chin-Chen Chang Department of Computer Science and Information Engineering, National United University, Taiwan

DOI:

https://doi.org/10.14738/aivp.1304.19230

Keywords:

Augmented reality, Occlusion handling, Depth prediction, Real-time rendering

Abstract

Due to the rapid development of mobile devices, augmented reality (AR) has been utilized increasingly, and thus applications of AR have increased for indoor and outdoor scenes. However, most of them focus on dealing with indoor scenes and the research for outdoor scenes is rare. In this paper, we propose a system to explore an application of AR for presenting 3D architecture models in outdoor scenes. We aim to enhance the realism of a 3D architectural model for an outdoor scene and provide users with a realistic experience. We first capture a color image through a camera and detect a plane on the image. When obtaining the detected plane, we can generate an anchor and attach virtual objects (3D architecture models) to this anchor. After that, we use the environmental high dynamic range mode of ARCore to obtain lighting information. Thus, our system can present a more realistic light and dark contrast and shadows on the virtual objects. Moreover, we propose an occlusion handling technique to obtain the correct relative positions of real objects and virtual objects. We use a depth prediction model to obtain depth information enabling the real objects to occlude the virtual objects. Finally, the processed augmented color image is rendered. The experiments demonstrate that our system can produce favorable results within a reasonable distance.

References

Azuma, R., A Survey of Augmented Reality. Presence: Teleoperators and Virtual Environments, 1997. 6(4): p. 355–385.

Rosenberg, L.B., Virtual Fixtures: Perceptual Tools for Telerobotic Manipulation. Proceedings of IEEE Virtual Reality Annual International Symposium, 1993: p. 76–82.

Abidin, S.Z., Omar, M.Z., and Tahir, N.M., Augmented Reality in Outdoor Settings: Evaluation of a Hybrid Image Recognition Technique. International Journal of Architectural Computing, 2021. 19(2): p. 180–196.

Bekele, M.K., Pierdicca, R., Frontoni, E., Malinverni, E.S., and Gain, J., Architecture MAR: A Mobile Augmented Reality System for Historical Building Reconstruction. ISPRS International Journal of Geo-Information, 2022. 7(12): p. 463.

Chiu, H.K., Zhang, Y., Mildenhall, B., Barron, J.T., and Srinivasan, P.P., Neural Light Field Estimation for Street Scenes with Differentiable Virtual Object Insertion. arXiv preprint, arXiv:2208.09480, 2022.

Fernández-Palacios, B.J., Morabito, D., and Remondino, F., LagunAR: A Mobile AR Application for Outdoor Visualization of Historical Architecture. Sensors, 2023. 23(21): p. 8905.

Fuchs, C., and Broll, W., GHAR: GeoPose-based Handheld Augmented Reality for Architectural Positioning, Manipulation and Visual Exploration. arXiv preprint, arXiv:2506.14414, 2025.

Google Developers, Updates to ARCore Help You Build More Interactive & Realistic AR Experiences. [Online] Available: https://developers.googleblog.com/2019/05/ARCoreIO19.html, May 2019.

Wofk, D., Ma, F., Yang, T., Karaman, S., and Sze, V., FastDepth: Fast Monocular Depth Estimation on Embedded Systems. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2019: p. 6101–6108.

Isnaeni, N.N., The Applicability of Augmented Reality (AR) and BIM Integration on Under Construction Project. Master Thesis, Department of Civil and Construction Engineering, National Yunlin University of Science & Technology, 2021.

Verykokou, S., Ioannidis, C., and Kontogianni, G., 3D Visualization via Augmented Reality: The Case of the Middle Stoa in the Ancient Agora of Athens. Proceedings of the 5th International Conference on Cultural Heritage, EuroMed, 2014: Limassol, Cyprus.

Durrant-Whyte, H., and Bailey, T., Simultaneous Localization and Mapping: Part I. IEEE Robotics and Automation Magazine, 2006. 13(2): p. 99–110.

Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., et al., MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv preprint, arXiv:1704.04861, 2017.

Yang, T.J., Howard, A., Chen, B., Zhang, X., Go, A., and Sadler, M., et al., NetAdapt: PlatformAware Neural Network Adaptation for Mobile Applications. Proceedings of the European Conference on Computer Vision (ECCV), 2018.

Silberman, N., Hoiem, D., Kohli, P., and Fergus, R., Indoor Segmentation and Support Inference from RGBD Images. Proceedings of the European Conference on Computer Vision (ECCV), 2012: p. 746–760.

Geiger, A., Lenz, P., Stiller, C., and Urtasun, R., Vision Meets Robotics: The KITTI Dataset. International Journal of Robotics Research, 2013.

Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., and Brox, T., FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017: p. 2462–2470.

Downloads

Published

2025-08-18

How to Cite

Lin, J.-W., & Chang, C.-C. (2025). An Augmented Reality System for Presenting Architecture Models in Outdoor Scenes. European Journal of Applied Sciences, 13(04), 271–280. https://doi.org/10.14738/aivp.1304.19230