Development of Three.js-based 3D Scenes with Seamless Visualisation of Gaussian Splatting and Transformation to Global Coordinates
DOI:
https://doi.org/10.12962/geoid.v20i2.8775Kata Kunci:
Distance Geometry, Gaussian Splatting, Position, three.js, SfM-MVSAbstrak
Existing scholarly literature on the Gaussian Splatting algorithm has predominantly concentrated on improving the rendering and reconstruction of three-dimensional objects, as well as exploring its applications in various academic disciplines, such as medicine, robotics, and mapping, while being limited to local coordinate systems. This study describes the development of a 3D scene modelled using the Gaussian Splatting algorithm, featuring accurate distance and position geometry based on three.js. The developed 3D scene was then evaluated with precise position and distance coordinates in the field and compared to the established SfM-MVS (Structure from Motion-Multi View Stereo) algorithm. The findings demonstrate that the proposed development successfully generated three.js-based 3D scenes with global coordinate compatibility, utilising the Gaussian Splatting algorithm, achieving the same level of position and distance accuracy as the SfM-MVS algorithm, with a 95% confidence level using a T-test. This research concludes that the developed approach is successful and can be further expanded for various scientific fields that require accurate position and distance information using the Gaussian Splatting Algorithm.
Referensi
Abramov, N., Lankegowda, H., Liu, S., Barazzetti, L., Beltracchi, C., and Ruttico, P. (2024): Implementing Immersive Worlds for Metaverse-Based Participatory Design through Photogrammetry and Blockchain, ISPRS International Journal of Geo-Information, 13(6). https://doi.org/10.3390/ijgi13060211
Apriansyah, M., and Harintaka (2023a): Pembuatan Model 3D Bangunan LoD3 Dengan Pemanfaatan Foto Udara dan Fotogrametri Terrestris Making 3D Building Models of LoD3 Using Aerial Photography and Terrestrial Photogrammetry, Geoid: Journal of Geodesy and Geomatics, 18(2), 243–252.
Apriansyah, M., and Harintaka, H. (2023b): Comparative Analysis of the Semantic Conditions of LoD3 3D Building Model Based on Aerial Photography and Terrestrial Photogrammetry, Journal of Applied Geospatial Information, 7(2), 927–931. https://doi.org/10.30871/jagi.v7i2.6634
Badan Informasi Geospasial (2018): Peraturan Kepala Badan Informasi Geospasial Nomor 6 Tahun 2018 Tentang Perubahan Atas Peraturan Kepala Badan Informasi Geospasial Nomor 15 Tahun 2014 Tentang Pedoman Teknis Ketelitian Peta Dasar, retrieved from internet: https://peraturan.bpk.go.id/Details/269444/peraturan-big-no-6-tahun-2018.
Balloni, E., Gorgoglione, L., Paolanti, M., Mancini, A., and Pierdicca, R. (2023): Few-shot photogrammetry: A comparison between nerf and mvs-sfm for the documentation of cultural heritage, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, International Society for Photogrammetry and Remote Sensing, 48, 155–162. https://doi.org/10.5194/isprs-Archives-XLVIII-M-2-2023-155-2023
Barba, S., Barbarella, M., Di Benedetto, A., Fiani, M., Gujski, L., and Limongiello, M. (2019): Accuracy assessment of 3d photogrammetric models from an unmanned aerial vehicle, Drones, 3(4), 1–19.
https://doi.org/10.3390/drones3040079
Cahyono, D. T., and Pratomo, D. G. (2008): Analisa Hasil Pengamatan Pasang Surut Air Laut Metode Langsung dan Tidak Langsung, Jurnal Geoid, retrieved from internet: https://iptek.its.ac.id/index.php/geoid/article/view/6962, 3(2), 130–138.
Chen, G., and Wang, W. (2024): A Survey on 3D Gaussian Splatting, retrieved from internet:
http://arxiv.org/abs/2401.03890, 1–20.
Chen, Y., and Wang, H. (2024): EndoGaussians: Single View Dynamic Gaussian Splatting for Deformable Endoscopic Tissues Reconstruction, retrieved September 17, 2024from internet: https://arxiv.org/abs/2401.13352v1.
Condorelli, F., Rinaudo, F., Salvadore, F., and Tagliaventi, S. (2021): A comparison between 3D reconstruction using nerf neural networks and MVS algorithms on cultural heritage images, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 43, 565–570.https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-565-2021
Dzulvikar, A. A. (2025): 3DGS Georeferenced Viewer - Azfa Ahmad Dzulvikar, , retrieved March 6, 2025, from internet: https://github.com/masazfaa/tesisazfa-3DGSViewer/tree/main.
El Barhoumi, N., Hajji, R., Bouali, Z., Ben Brahim, Y., and Kharroubi, A. (2022): Assessment of 3D Models Placement Methods in Augmented Reality, Applied Sciences (Switzerland), 12(20). https://doi.org/10.3390/app122010620
Elkhrachy, I. (2021): Accuracy Assessment of Low-Cost Unmanned Aerial Vehicle (UAV) Photogrammetry, Alexandria Engineering Journal, 60(6), 5579–5590. https://doi.org/10.1016/j.aej.2021.04.011
Fei, B., Xu, J., Zhang, R., Zhou, Q., Yang, W., and He, Y. (2024): 3D Gaussian Splatting as New Era: A Survey, IEEE Transactions on Visualization and Computer Graphics, PP(8), 1–20. https://doi.org/10.1109/TVCG.2024.3397828
Ferreira, J. E. V., Pinheiro, M. T. S., dos Santos, W. R. S., and Maia, R. da S. (2016): Graphical representation of chemical periodicity of main elements through boxplot, Educacion Quimica, 27(3), 209–216.
https://doi.org/10.1016/j.eq.2016.04.007
Gao, L., Zhao, Y., Han, J., and Liu, H. (2022): Research on Multi-View 3D Reconstruction Technology Based on SFM, Sensors, 22(12). https://doi.org/10.3390/s22124366
Gao, S., Gan, S., Yuan, X., Bi, R., Li, R., Hu, L., and Luo, W. (2022): Experimental study on 3D measurement accuracy detection of low altitude uav for repeated observation of an invariant surface, Processes, 10(1).
https://doi.org/10.3390/pr10010004
Ghilani, C. D. (2017): Adjustment Computations, Adjustment Computations, John Wiley & Sons, Inc.
https://doi.org/10.1002/9781119390664
Gómez-Gutié Rrez, Á., Juan De Sanjosé -Blasco, J., Lozano-Parra, J., Berenguer-Sempere, F., De Matí As-Bejarano, J.,
Abellan, A., Jaboyedoff, M., Derron, M.-H., Kerle, N., and Thenkabail, P. S. (2015): Does HDR Pre-Processing Improve the Accuracy of 3D Models Obtained by Means of two Conventional SfM-MVS Software Packages? The Case of the Corral del Veleta Rock Glacier, Remote Sensing 2015, Vol. 7, Pages 10269-10294, 7(8), 10269–10294. https://doi.org/10.3390/RS70810269
Hillman, S., Wallace, L., Reinke, K., and Jones, S. (2021): A comparison between TLS and UAS LiDAR to represent eucalypt crown fuel characteristics, ISPRS Journal of Photogrammetry and Remote Sensing, 181(September), 295–307. https://doi.org/10.1016/j.isprsjprs.2021.09.008
Karnawat, K., Choudhari, H., Saxena, A., Singal, M., and Gadam, R. (2023): 3D reconstruction using Structure for Motion, retrieved August 7, 2024from internet: https://arxiv.org/abs/2306.06360v1.
Kellog, M. (2025): Releases · mkkellogg/GaussianSplats3D, , retrieved April 23, 2025, from internet:
https://github.com/mkkellogg/GaussianSplats3D/releases.
Kerbl, B., Kopanas, G., Leimkuehler, T., and Drettakis, G. (2023): 3D Gaussian Splatting for Real-Time Radiance Field Rendering, ACM Transactions on Graphics, 42(4), 1–14. https://doi.org/10.1145/3592433
Kovanič, L., Štroner, M., Blistan, P., Urban, R., and Boczek, R. (2023): Combined ground-based and UAS SfM-MVS approach for determination of geometric parameters of the large-scale industrial facility – Case study, Measurement: Journal of the International Measurement Confederation, 216, 112994.
https://doi.org/10.1016/j.measurement.2023.112994
Li, Q., Yang, G., Gao, C., Huang, Y., Zhang, J., Huang, D., Zhao, B., Chen, X., and Chen, B. M. (2024): Single dronebased 3D reconstruction approach to improve public engagement in conservation of heritage buildings: A case of Hakka Tulou, Journal of Building Engineering, 87(March), 108954. https://doi.org/10.1016/j.jobe.2024.108954
Liu, Y., Li, C., Yang, C., and Yuan, Y. (2024): EndoGaussian: Real-time Gaussian Splatting for Dynamic Endoscopic Scene Reconstruction, retrieved September 17, 2024from internet: https://arxiv.org/abs/2401.12561v2.
Luo, J., Huang, T., Wang, W., and Feng, W. (2024): A review of recent advances in 3D Gaussian Splatting for optimization and reconstruction, Image and Vision Computing, 151(May), 105304.
https://doi.org/10.1016/j.imavis.2024.105304
Malarz, D., Smolak, W., Tabor, J., Tadeja, S., and Spurek, P. (2023): Gaussian Splatting with NeRF-based Color and Opacity, Computer Vision and Image Understanding, 251(July 2024), 104273.
https://doi.org/10.1016/j.cviu.2024.104273
Mandaya, I. (2020): ( Unmanned Aerial Vehicle ) Untuk Identifikasi Dan Klasifikasi Jenis - Jenis Kerusakan Jalan, 14(3), 162–172.
Matsuki, H., Murai, R., Kelly, P. H. J., and Davison, A. J. (2023): Gaussian Splatting SLAM, retrieved from internet: http://arxiv.org/abs/2312.06741, 18039–18048.
Mbuli, N., Mendu, B., and Pretorius, J. H. C. (2022): Statistical analysis of forced outage duration data for subtransmission circuit breakers, Energy Reports, 8, 1424–1433. https://doi.org/10.1016/j.egyr.2022.09.131
McDermott, M., and Rife, J. (2024): ICET Online Accuracy Characterization for Geometry-Based Laser Scan Matching, Navigation, Journal of the Institute of Navigation, 71(2). https://doi.org/10.33012/navi.647
Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R., and Ng, R. (2022): NeRF: Representing
Scenes as Neural Radiance Fields for View Synthesis, Communications of the ACM, 65(1), 99–106.
https://doi.org/10.1145/3503250
Mokroš, M., Mikita, T., Singh, A., Tomaštík, J., Chudá, J., Wężyk, P., Kuželka, K., Surový, P., Klimánek, M., Zięba-
Kulawik, K., Bobrowski, R., and Liang, X. (2021): Novel low-cost mobile mapping systems for forest inventories
as terrestrial laser scanning alternatives, International Journal of Applied Earth Observation and Geoinformation,
104, 102512. https://doi.org/10.1016/j.jag.2021.102512
Morita, M. M., Loaiza Carvajal, D. A., González Bagur, I. L., and Bilmes, G. M. (2024): A combined approach of SFMMVS
photogrammetry and reflectance transformation imaging to enhance 3D reconstructions, Journal of Cultural
Heritage, 68, 38–46. https://doi.org/10.1016/j.culher.2024.05.008
mrdoob (2024): mrdoob/three.js: JavaScript 3D Library., retrieved February 8, 2025, from internet:
https://github.com/mrdoob/three.js/.
Murtiyoso, A., Markiewicz, J., Karwel, A. K., Grussenmeyer, P., and Kot, P. (2024): Comparison of state-of-the-art multiview
stereo solutions for close range heritage documentation, International Archives of the Photogrammetry,
Remote Sensing and Spatial Information Sciences, 48(2), 317–323. https://doi.org/10.5194/ISPRS-ARCHIVESXLVIII-
2-W4-2024-317-2024
Negara, T. B., and Harintaka (2021): Pemodelan Bangunan 3D Menggunakan Footprint Bangunan Hasil Ekstraksi Mask
R-CNN dan Dense Point Cloud dari Foto Udara UAV, Prosiding FIT ISI Vol 1, 2021 (248-260), 1, 248–260.
Petrovska, I., Jäger, M., Haitz, D., and Jutzi, B. (2023): Geometric Accuracy Analysis Between Neural Radiance Fields
(NeRF) And Terrestrial Laser Scanning (TLS), International Archives of the Photogrammetry, Remote Sensing and
Spatial Information Sciences - ISPRS Archives, Copernicus GmbH, 48, 153–159. https://doi.org/10.5194/isprsarchives-
XLVIII-1-W3-2023-153-2023
Pham, H. T., Claessens, S., Kuhn, M., and Awange, J. (2023): Performance evaluation of high/ultra-high-degree global
geopotential models over Vietnam using GNSS/leveling data, Geodesy and Geodynamics, 14(5), 500–512.
https://doi.org/10.1016/j.geog.2023.03.002
Previtali, M., Barazzetti, L., and Roncoroni, F. (2024): Orthophoto generation with gaussian splatting : mitigating
reflective surface artifacts in imagery from low-cost sensors, XLVIII(December), 12–13.
Qin, M., Li, W., Zhou, J., Wang, H., and Pfister, H. (2023): LangSplat: 3D Language Gaussian Splatting, 20051–20060.
https://doi.org/10.1109/CVPR52733.2024.01895
Rabby, A. S. A., and Zhang, C. (2023): BeyondPixels: A Comprehensive Review of the Evolution of Neural Radiance
Fields, Journal of the ACM, 37(111), 33. https://doi.org/https://doi.org/10.48550/arXiv.2306.03000
Sannholm, B. (2024): Real-Time Novel-View Synthesis for the Web Using 3D Gaussian Splatting Exploring Mesh-
Supervised 3D Gaussian Scene Optimization and Efficient Web Rendering for Product Visualization, retrieved
from internet: https://hdl.handle.net/2077/83678.
Tancik, M., Weber, E., Ng, E., Li, R., Yi, B., Kerr, J., Wang, T., Kristoffersen, A., Austin, J., Salahi, K., Ahuja, A.,
McAllister, D., and Kanazawa, A. (2023): Nerfstudio: A Modular Framework for Neural Radiance Field
Development, Proceedings - SIGGRAPH 2023 Conference Papers. https://doi.org/10.1145/3588432.3591516
Tang, J., Zhou, H., Chen, X., Hu, T., Ding, E., Wang, J., and Zeng, G. (2023): Delicate Textured Mesh Recovery from
NeRF via Adaptive Surface Refinement, Proceedings of the IEEE International Conference on Computer Vision,
17693–17703. https://doi.org/10.1109/ICCV51070.2023.01626
Tavani, S., Granado, P., Corradetti, A., Girundo, M., Iannace, A., Arbués, P., Muñoz, J. A., and Mazzoli, S. (2014):
Building a virtual outcrop, extracting geological information from it, and sharing the results in Google Earth via
OpenPlot and Photoscan: An example from the Khaviz Anticline (Iran), Computers and Geosciences, 63, 44–53.
https://doi.org/10.1016/j.cageo.2013.10.013
Usud, A., and Sukojo, B. M. (2014): Analisis Pengaruh Tutupan Lahan Terhadap Ketelitian Aster Gdem V2 Dan Dem
Srtm V4.1 (Studi Kasus: Kota Batu, Kabupaten Malang, Jawa Timur), Geoid, 10(1), 8.
https://doi.org/10.12962/j24423998.v10i1.584
Warburg, F., Weber, E., Tancik, M., Holynski, A., and Kanazawa, A. (2023): Nerfbusters: Removing Ghostly Artifacts
from Casually Captured NeRFs, Proceedings of the IEEE International Conference on Computer Vision, Institute
of Electrical and Electronics Engineers Inc., 18074–18084. https://doi.org/10.1109/ICCV51070.2023.01661
Wu, T., Yuan, Y. J., Zhang, L. X., Yang, J., Cao, Y. P., Yan, L. Q., and Gao, L. (2024): Recent advances in 3D Gaussian splatting, Computational Visual Media, 10(4), 613–642. https://doi.org/10.1007/s41095-024-0436-y
Xie, Y., Teo, M. X., Li, S., Huang, L., Liang, N., and Cai, Y. (2023): As-built BIM reconstruction of piping systems using
smartphone videogrammetry and terrestrial laser scanning, Automation in Construction, 156, 105120.
https://doi.org/10.1016/j.autcon.2023.105120
Zainuddin, K., Ghazali, M. D., Marzukhi, F., Samad, A. M., Ariff, M. F. M., and Majid, Z. (2024): Evaluation of nerf 3d
reconstruction for rock art documentation, International Archives of the Photogrammetry, Remote Sensing and
Spatial Information Sciences - ISPRS Archives, Copernicus GmbH, 48, 469–475. https://doi.org/10.5194/isprs-
Archives-XLVIII-2-W4-2024-469-2024
Zhao, H., Zhao, X., Zhu, L., Zheng, W., and Xu, Y. (2024): HFGS: 4D Gaussian Splatting with Emphasis on Spatial and
Temporal High-Frequency Components for Endoscopic Scene Reconstruction, retrieved September 17, 2024from
internet: https://arxiv.org/abs/2405.17872v3.
Zhou, L., Meng, R., Tan, Y., Lv, Z., Zhao, Y., Xu, B., and Zhao, F. (2022): Comparison of UAV-based LiDAR and digital
aerial photogrammetry for measuring crown-level canopy height in the urban environment, Urban Forestry and
Urban Greening, 69(November 2021), 127489. https://doi.org/10.1016/j.ufug.2022.127489
