簡易檢索 / 詳目顯示

回結果列表
研究生： 瑞勒斯
Reffriandi Aurellius Ansell
論文名稱： 建構及搜尋3D物件 應用於虛擬實境平台之架構 — 以室內設計為例
The Framework of Creating and Searching 3D Objects in the VR Platform: A Case Study of Interior Design
指導教授： 馮重偉
Feng, Chung-Wei
學位類別： 碩士
Master
系所名稱： 工學院 - 土木工程學系
Department of Civil Engineering
論文出版年： 2023
畢業學年度： 111
語文別： 英文
論文頁數： 67
外文關鍵詞： Interior Design, Virtual Reality, Obejct Detection, 3D Reconstruction
相關次數： 點閱：65  下載：2 
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報

    • Implementing Virtual Reality (VR) technology into real estate proved beneficial in terms of time and cost efficiency. In addition, VR technology also believes in befitting interior and architectural design. The regular application of developing a VR scene was to create a synthetic digital model generated by 3D modeling software as its virtual environment. However, it ensues in a vague feeling of realism. Therefore, a 3D reconstructed model, captured and presented to mimic real-world visualization, can be used as a VR virtual environment scene substitute. The Gibson Environment dataset provides the 3D indoor space model and the 3D-FUTURE as the interior object library to build the VR app. Then, data management was performed using the YOLOv5 object detection task to comprehend the dataset. Moreover, another technique to achieve the 3D model reconstruction incorporating the iPhone14 Pro Light Detection and Ranging (LiDAR) sensor camera and third-party app, Polycam, was done to expand the database. This study integrates the organized dataset into the VR development process, resulting in an effective VR interior design simulation system.

    ABSTRACT i ACKNOWLEDGEMENTS ii TABLE OF CONTENTS iii LIST OF TABLES v LIST OF FIGURES vi CHAPTER 1: INTRODUCTION 1 1.1 Background and Motivation 1 1.2 Objectives 4 1.3 Research Scope 5 1.4 Research Process 7 1.5 Thesis Structure 9 CHAPTER 2: PROBLEM STATEMENT AND LITERATURE REVIEW 10 2.1 Problem Statement 10 2.2 Literature Review 10 2.2.1 VR Development Practices in Real Estate and Interior Design 10 2.2.2 Indoor Model Construction Method for VR Environment 11 2.2.3 Challenge in Organizing Interior Library Data 12 2.2.4 YOLOv5 Object Detection Implementation for Interior Library Data Management 13 2.2.5 Utilization of iPhone Camera and Polycam Software as an Alternative to Generate 3D Reconstructed Model 15 2.3 Summary 16 CHAPTER 3: METHODOLOGY 18 3.1 Dataset Collection 20 3.1.1 Gibson Environment 20 3.1.2 3D-FUTURE 21 3.2 Dataset Processing Tools 22 3.3 Furniture Object Detection Task 24 3.3.1 Employing Roboflow to Preprocess Furniture Image Database 24 3.3.2 Utilizing the YOLOv5 Algorithm to Train Preprocessed Dataset 27 3.4 Furniture Dataset Management 30 3.5 VR App Development 31 3.6 Capturing and Generating 3D Reconstructed Model 36 CHAPTER 4: RESEARCH FRAMEWORK 39 4.1 Framework For Reconstructing 3D Objects Using iPhone 14 Pro and Polycam App 42 4.2 Framework For Utilizing Object Detection Task for Library Management 44 4.3 Framework For VR App Development 46 4.4 Summary 48 CHAPTER 5: RESULT AND DISCUSSION 49 5.1 Polycam Reconstructed 3D Objects 49 5.2 Furniture Library Management 50 5.2.1 YOLOv5 Object Detection Training Model Performance 50 5.2.2 YOLOv5 Object Detection Validation Performance 52 5.3 VR App Development Results 52 5.3.1 Implementing Trained Model As An Object Searching Mechanism 58 5.4 Summary 60 CHAPTER 6: CONCLUSIONS AND RECOMMENDATIONS 61 6.1 Conclusions 61 6.2 Recommendations 63 REFERENCES 65

    Azmi, A., Ibrahim, R., Abdul Ghafar, M., & Rashidi, A. (2022). Smarter real estate marketing using virtual reality to influence potential homebuyers’ emotions and purchase intention. Smart and Sustainable Built Environment, 11(4), 870–890. https://doi.org/10.1108/SASBE-03-2021-0056
    Ball, C., Huang, K. T., & Francis, J. (2021). Virtual reality adoption during the COVID-19 pandemic: A uses and gratifications perspective. Telematics and Informatics, 65. https://doi.org/10.1016/j.tele.2021.101728
    Cao, K., & Li, L. (2019). Research on the Application of VR Technology in Interior Design. Advances in Social Science, Education and Humanities Research, 356, 745–748. https://doi.org/10.2991/cesses-19.2019.168
    Díaz-Vilariño, L., Tran, H., Frías, E., Balado, J., & Khoshelham, K. (2022). 3D MAPPING OF INDOOR AND OUTDOOR ENVIRONMENTS USING APPLE SMART DEVICES. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 43(B4-2022), 303–308. https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-303-2022
    Fu, H., Cai, B., Gao, L., Zhang, L.-X., Wang, J., Li, C., Zeng, Q., Sun, C., Jia, R., Zhao, B., & Zhang, H. (2021). 3D-FRONT: 3D Furnished Rooms with layOuts and semaNTics. Proceedings of the IEEE/CVF International Conference on Computer Vision, 10933–10942. https://doi.org/https://doi.org/10.48550/arXiv.2011.09127
    Fu, H., Jia, R., Gao, L., Gong, M., Zhao, B., Maybank, S., & Tao, D. (2021). 3D-FUTURE: 3D Furniture Shape with TextURE. International Journal of Computer Vision, 129(12), 3313–3337. https://doi.org/10.1007/s11263-021-01534-z
    Jakovljević, G., & Taboada, F. A. (2022). IPHONE 13 PRO VS PROFESSIONAL TLS FOR 3D INDOOR MAPPING. PROCEEDINGSOF INTERNATIONAL CONFERENCE ON CONTEMPORARY THEORY AND PRACTICE IN CONSTRUCTION XV. https://doi.org/10.7251/STP2215274J
    Kabaivanov, S., & Markovska, V. (2021). Artificial intelligence in real estate market analysis. AIP Conference Proceedings, 2333. https://doi.org/10.1063/5.0041806
    Kalantari, S., & Neo, J. R. J. (2020). Virtual Environments for Design Research: Lessons Learned From Use of Fully Immersive Virtual Reality in Interior Design Research. Journal of Interior Design, 45(3), 27–42. https://doi.org/10.1111/joid.12171
    Kamil, M. H. F. M., Yahya, N., Abidin, I. S. Z., & Norizan, A. R. (2021). DEVELOPMENT OF VIRTUAL REALITY TECHNOLOGY: HOME TOUR FOR REAL ESTATE PURCHASE DECISION MAKING. Malaysian Journal of Computer Science, 2021(Special Issue 1), 85–93. https://doi.org/10.22452/mjcs.sp2021no1.8
    Kang, H. J., Shin, J. hye, & Ponto, K. (2020). How 3D Virtual Reality Stores Can Shape Consumer Purchase Decisions: The Roles of Informativeness and Playfulness. Journal of Interactive Marketing, 49, 70–85. https://doi.org/10.1016/j.intmar.2019.07.002
    Luetzenburg, G., Kroon, A., & Bjørk, A. A. (2021). Evaluation of the Apple iPhone 12 Pro LiDAR for an Application in Geosciences. Scientific Reports, 11(1). https://doi.org/10.1038/s41598-021-01763-9
    Nazir, A., & Wani, Mohd. A. (2023). You Only Look Once - Object Detection Models: A Review. 10th International Conference on Computing for Sustainable Global Development (INDIACom), 1088–1095.
    Phommaly, K., & Yu, L. (2020). Research of virtual reality technology in home design. Journal of Physics: Conference Series, 1624(6). https://doi.org/10.1088/1742-6596/1624/6/062017
    Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 779–788. https://doi.org/10.1109/CVPR.2016.91
    Teppati Losè, L., Spreafico, A., Chiabrando, F., & Giulio Tonolo, F. (2022). Apple LiDAR Sensor for 3D Surveying: Tests and Results in the Cultural Heritage Domain. Remote Sensing, 14(17). https://doi.org/10.3390/rs14174157
    Vacca, G. (2023). T. Heritage, 6(2), 1476–1501. https://doi.org/10.3390/heritage6020080
    Ververidis, D., Nikolopoulos, S., & Kompatsiaris, I. (2022). A Review of Collaborative Virtual Reality Systems for the Architecture, Engineering, and Construction Industry. Architecture, 2(3), 476–496. https://doi.org/10.3390/architecture2030027
    Wedel, M., Bigné, E., & Zhang, J. (2020). Virtual and augmented reality: Advancing research in consumer marketing. International Journal of Research in Marketing, 37(3), 443–465. https://doi.org/10.1016/j.ijresmar.2020.04.004
    Xia, F., Zamir, A. R., He, Z., Sax, A., Malik, J., & Savarese, S. (2018). Gibson Env: Real-World Perception for Embodied Agents. Computer Vision and Pattern Recognition 2018, 9069–9079. https://doi.org/https://doi.org/10.48550/arXiv.1808.10654
    Xu, R., Lin, H., Lu, K., Cao, L., & Liu, Y. (2021). A forest fire detection system based on ensemble learning. Forests, 12(2), 1–17. https://doi.org/10.3390/f12020217
    Zhao, Z. Q., Zheng, P., Xu, S. T., & Wu, X. (2019). Object Detection with Deep Learning: A Review. In IEEE Transactions on Neural Networks and Learning Systems (Vol. 30, Issue 11, pp. 3212–3232). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/TNNLS.2018.2876865

    下載圖示 校內：立即公開
    校外：立即公開
    QR CODE