臺灣博碩士論文加值系統

本研究針對中式傳統圖騰藝術物件，研發低成本資料獲取方法結合友善且開源平台之最佳流程策略，進而建構點雲與三維模型。由於建構物件呈現不規則且複雜表面之形狀，故採取多視角擷取策略。研發流程透過點雲產製與資料處理等過程，進行測試和驗證。初始點雲經過三個不確定因素評估品質及設定：1) 物件與RGB-D感測器間之距離(幾何因素)，2)適合之反射照度與入射照度(環境因素)，3)同一測站但不同高度之取樣間距(幾何因素)。本研究使用Intel RealSense L515 RGB-D感測器作為低成本資料獲取設備(一台約12,000新台幣) ，據以得到深度影像和點雲。
確認每站點雲品質後，再利用MeshLab與Cloud-to-Compare等兩套免費開源軟體進行多站點雲套合及三維模型建置。最終，總結與分析本研究提出低成本RGB-D感測器之方法和流程、點雲品質、模型建置、參數設定等條件，給予適當的建議。

With the increasing need for digital documentation for cultural heritage worldwide, the technologies of 3D capture and data acquisition have gradually been playing a very important role. This study tried to develop an optimum 3D modeling strategy that combined a low-cost approach and user-friendly open-source platforms for objects with complex patterns.
In this study, Intel RealSense L515 RGB-D Camera Sensor as an innovative low-cost laser scanner (costs around US$ 406.00), and MeshLab and Cloud-to-Compare as well-known open-source platforms, were adopted to generate point cloud and 3D model for a selected art object with Chinese traditional patterns. The multi-viewing approach was applied because of the curvy shape of the object’s surface. A series of experiments was established to clarify and verify the quality of final digital performance, including 1) distance between the object and the RGB-D camera sensor, 2) environmental illuminance, and 3) height positioning from an orthogonal single view.
A strategic approach was developed and confirmed based on the evaluation of the experiment above, which proves that the combination of the low-cost RGB-D camera sensor and open-source platforms is an alternative to producing a 3D model with high quality for the cost range used. The results showed that the developed strategy presents the optimum conditions for objects with irregular patterns with a low-cost approach.

Acknowledgments iv
摘 要 v
Abstract vi
List of Figures ix
List of Tables xi
List of Abbreviations xii
Chapter 1. Introduction - 1 -
1.1 Motivation and Background - 1 -
1.2 Research Objectives and Scope - 4 -
1.3 Innovations and Contributions - 6 -
Chapter 2. Literature Review - 9 -
2.1 Three-Dimensional Laser Scanning Devices and Surveying Techniques - 9 -
2.1.1 RGB-D Camera Sensors - 10 -
2.1.2 Time-of-Flight (TOF) Depth Camera - 10 -
2.1.3 Low-End Portable Scanners - 12 -
2.1.4 Laser Scanning Technique Assessment - 13 -
2.2 3D Modeling Standard Guidelines and Regulations - 16 -
2.2.1 VDI/VDE Standard Guidelines - 16 -
2.2.2 ISO Standards - 17 -
2.2.3 Testing Procedure Description - 17 -
2.3 Software and Tools - 20 -
2.3.1 Software Solutions for 3D Model Reconstruction - 20 -
2.3.2 Iterative Closest Point Algorithm - 21 -
2.3.3 Screened Poisson Reconstruction - 22 -
2.4 Summary - 23 -
Chapter 3. Procedure and Methodology - 25 -
3.1 Study Site and Research Object - 25 -
3.2 Materials and Software - 27 -
3.2.1 Equipment and Instruments Description - 27 -
3.2.2 Software and Digital Processing Tools - 32 -
3.3 Methodology - 33 -
3.3.1 Procedure and Structure - 33 -
3.3.2 Phase 1: Raw Captured Data - 37 -
3.3.3 Phase 2: Point Cloud Generation - 45 -
3.3.4 Phase 3: 3D Model Reconstruction - 49 -
Chapter 4. Results and Discussion - 50 -
4.1 Raw Captured Data Experimental Results - 50 -
4.1.1. RGB-D – Tripod Vessel Distance Errors Assessment - 51 -
4.1.2 Incident Environmental Illuminance & Point Sampling Frequency - 54 -
4.1.3 Height Positioning and FOV Overlapping - 60 -
4.2 Point Cloud Generation Assessment - 63 -
4.2.1 Raw Data Acquisition and PC Generation Summary Results. - 64 -
4.2.2 ICP Algorithm Multi-viewing PC Production - 65 -
4.3 3D Model Reconstruction Evaluative Results - 70 -
4.4 Discussion - 72 -
4.4.1 Tripod Vessel Reconstruction Strategy - 73 -
4.4.2 External RGB-D Experimental Considerations - 78 -
4.4.3 Low-Cost Strategic Approach Resolution - 82 -
Chapter 5. Conclusion and Recommendations - 83 -
5.1 Achievements - 83 -
5.2 Recommendations - 85 -
References - 87 -

1.Abate, D. (2019). Built-heritage multi-temporal monitoring through photogrammetry and 2D/3D change detection algorithms. Studies in Conservation, 64(7), 423-434.
2.Altman, S., Xiao, W., & Grayson, B. (2017). Evaluation of low-cost terrestrial photogrammetry for 3D reconstruction of complex buildings. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, 4.
3.Antón, D., Medjdoub, B., Shrahily, R., & Moyano, J. (2018). Accuracy evaluation of the semi-automatic 3D modeling for historical building information models. International Journal of Architectural Heritage, 12(5), 790-805.
4.Antón, D., Pineda, P., Medjdoub, B., & Iranzo, A. (2019). As-built 3D heritage city modelling to support numerical structural analysis: Application to the assessment of an archaeological remain. Remote Sensing, 11(11), 1276.
5.Asvadi, A., Garrote, L., Premebida, C., Peixoto, P., & Nunes, U. J. (2018). Multimodal vehicle detection: fusing 3D-LIDAR and color camera data. Pattern Recognition Letters, 115, 20-29.
6.Benedek, C., Majdik, A., Nagy, B., Rozsa, Z., & Sziranyi, T. (2021). Positioning and perception in LIDAR point clouds. Digital Signal Processing, 119, 103193.
7.Caciora, T., Herman, G. V., Ilieș, A., Baias, Ș., Ilieș, D. C., Josan, I., & Hodor, N. (2021). The use of virtual reality to promote sustainable tourism: A case study of wooden churches historical monuments from Romania. Remote Sensing, 13(9), 1758.
8.Capolupo, A. (2021). Accuracy Assessment of Cultural Heritage Models Extracting 3D Point Cloud Geometric Features with RPAS SfM-MVS and TLS Techniques. Drones, 5(4), 145.
9.Carfagni, M., Furferi, R., Governi, L., Servi, M., Uccheddu, F., & Volpe, Y. (2017). On the performance of the Intel SR300 depth camera: metrological and critical characterization. IEEE Sensors Journal, 17(14), 4508-4519.
10.Chen, C., Jafari, R., & Kehtarnavaz, N. (2017). A survey of depth and inertial sensor fusion for human action recognition. Multimedia Tools and Applications, 76(3), 4405-4425.
11.Chiabrando, F., Chiabrando, R., Piatti, D., & Rinaudo, F. (2009). Sensors for 3D imaging: Metric evaluation and calibration of a CCD/CMOS time-of-flight camera. Sensors, 9(12), 10080-10096.
12.Chiabrando, F., Sammartano, G., Spanò, A., & Spreafico, A. (2019). Hybrid 3D models: When geomatics innovations meet extensive built heritage complexes. ISPRS International Journal of Geo-Information, 8(3), 124.
13.Dlesk, A., Vach, K., & Holubec, P. (2019). Analysis of Possibilities of Low-Cost Photogrammetry for Interior Mapping. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 27-31.
14.Drouin, M. A., & Seoud, L. (2020). Consumer-Grade RGB-D Cameras. In 3D Imaging, Analysis and Applications (pp. 215-264). Springer, Cham.
15.Fernandez, J. C., Singhania, A., Caceres, J., Slatton, K. C., Starek, M., & Kumar, R. (2007). An overview of lidar point cloud processing software. GEM Center Report No. Rep_2007-12-001, University of Florida, 27.
16.Genovese, R. A. (2005). Architectural, archaeologic and environmental restoration planning methodology: historic researches and techniques of survey aiming to conservation. In Proc. CIPA (Vol. 5, pp. 295-299).
17.Georgopoulos, A., & Ioannidis, C. (2004). Photogrammetric and surveying methods for the geometric recording of archaeological monuments. FIG Working Week (Vol. 22, p. 27).
18.Ghilani, C. D., & Wolf, P. R. (2012). Elementary surveying. An Introduction to Geomatics. Edition: 13th. Upper Saddle River, New Jersey: Pearson Education.
19.Guidi, G., Remondino, F., Morlando, G., Del Mastio, A., Uccheddu, F., & Pelagotti, A. (2007). Performances evaluation of a low-cost active sensor for cultural heritage documentation. 8th Conference on Optical 3D Measurement Techniques (pp. 59-69).
20.Guidi, G., Gonizzi Barsanti, S., & Micoli, L. L. (2016). 3D capturing performances of low-cost range sensors for mass-market applications. 23rd International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Congress, ISPRS 2016 (pp. 33-40). International Society for Photogrammetry and Remote Sensing.
21.Historic England. (2017). Photogrammetric Applications for Cultural Heritage. Guidance for Good Practice. Swindon. Historic England.
22.JJP A&P (2014). Xue Si Building, Feng Chia University. JJP Architects and Planners Portafolio. https://www.jjpan.cn/en/portfolio/xue-si-building-feng-chia-university/
23.Horaud, R., Hansard, M., Evangelidis, G., & Ménier, C. (2016). An overview of depth cameras and range scanners based on time-of-flight technologies. Machine vision and applications, 27(7), 1005-1020.
24.ISO. (2021). Standard ISO 13060-13:2021. 1st Edition 2021-09. Reference Number: ISO 13060-13:2021(E). Switzerland: International Organization for Standards.
25.Kadobayashi, R., Kochi, N., Otani, H., & Furukawa, R. (2004). Comparison and evaluation of laser scanning and photogrammetry and their combined use for digital recording of cultural heritage. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 35(5), 401-406.
26.Kazhdan, M., Bolitho, M., & Hoppe, H. (2006). Poisson surface reconstruction. Proceedings of the fourth Eurographics symposium on Geometry processing (Vol. 7).
27.Kazhdan, M., & Hoppe, H. (2013). Screened poisson surface reconstruction. ACM Transactions on Graphics (ToG), 32(3), 1-13.
28.Kushwaha, S. K. P., Dayal, K. R., Raghavendra, S., Pande, H., Tiwari, P. S., Agrawal, S., & Srivastava, S. K. (2020). 3D Digital documentation of a cultural heritage site using terrestrial laser scanner—A case study. Applications of Geomatics in Civil Engineering (pp. 49-58). Springer, Singapore.
29.Lachat, E., Landes, T., & Grussenmeyer, P. (2019). Comparison of point cloud registration algorithms for better result assessment–towards an open-source solution. ISPRS TC II Mid-term Symposium “Towards Photogrammetry 2020 (Vol. 42, pp. 551-558). Copernicus Publications.
30.Li, J., Qian, F., & Chen, X. (2020). Point cloud registration algorithm based on overlapping region extraction. Journal of Physics: Conference Series (Vol. 1634, No. 1, p. 012012). IOP Publishing.
31.Li, L. (2014). Time-of-flight camera–an introduction. Technical white paper, (SLOA190B).
32.Li, P., Wang, R., Wang, Y., & Tao, W. (2020). Evaluation of the ICP algorithm in 3D point cloud registration. IEEE Access, 8, 68030-68048.
33.Lourenço, F., & Araujo, H. (2021). Intel RealSense SR305, D415 and L515: Experimental Evaluation and Comparison of Depth Estimation. VISIGRAPP (4: VISAPP) (pp. 362-369).
34.Moyano, J., Nieto-Julián, J. E., Bienvenido-Huertas, D., & Marín-García, D. (2020). Validation of close-range photogrammetry for architectural and archaeological heritage: Analysis of point density and 3D mesh geometry. Remote Sensing, 12(21), 3571.
35.Nex, F., & Rinaudo, F. (2010). Photogrammetric and LiDAR integration for the cultural heritage metric surveys. International archives of photogrammetry, remote sensing and spatial information sciences, 38(Part 5), 490-495.
36.Paoli, A., Neri, P., Razionale, A. V., Tamburrino, F., & Barone, S. (2020). Sensor architectures and technologies for upper limb 3D surface reconstruction: a review. Sensors, 20(22), 6584.
37.Rabbani, T., Van Den Heuvel, F., & Vosselmann, G. (2006). Segmentation of point clouds using smoothness constraint. International archives of photogrammetry, remote sensing and spatial information sciences, 36(5), 248-253.
38.Řezníček, J., & Pavelka, K. (2008). New Low-cost 3D Scanning Techniques for Cultural Heritage Documentation. The International Archives of the Photogrammetry. Remote Sensing and Spatial Information Sciences, 37, 237-240.
39.Servi, M., Mussi, E., Profili, A., Furferi, R., Volpe, Y., Governi, L., & Buonamici, F. (2021). Metrological Characterization and Comparison of D415, D455, L515 RealSense Devices in the Close Range. Sensors, 21(22), 7770.
40.Tatoglu, A., & Pochiraju, K. (2012). Point cloud segmentation with LIDAR reflection intensity behavior. 2012 IEEE International Conference on Robotics and Automation (pp. 786-790). IEEE.
41.VDI. (2012). VDI/VDE 2634 Blatt 2/Blatt 3. 2021 English Edition. Berlin: Verein Deutscher Ingenieure e.V.
42.Wolf, P. R., Dewitt, B. A., & Wilkinson, B. E. (2014). Elements of Photogrammetry with Applications in GIS. McGraw-Hill Education.
43.Worboys, M. (2011). Modeling indoor space. Proceedings of the 3rd ACM SIGSPATIAL international workshop on indoor spatial awareness (pp. 1-6).
44.Xin, W., & Pu, J. (2010). An improved ICP algorithm for point cloud registration. 2010 International Conference on Computational and Information Sciences (pp. 565-568). IEEE
45.Yilmaz, H. M., Yakar, M., Gulec, S. A., & Dulgerler, O. N. (2007). Importance of digital close-range photogrammetry in documentation of cultural heritage. Journal of Cultural Heritage, 8(4), 428-433.
46.Zlatanova, S., Sithole, G., Nakagawa, M., & Zhu, Q. (2013). Problems in indoor mapping and modelling. Acquisition and Modelling of Indoor and Enclosed Environments 2013, Cape Town, South Africa, 11-13 December 2013, ISPRS Archives Volume XL-4/W4, 2013.