Development of Cloud Point Data Processing Program for 3D BIM and 2D Cross Section Needs

Authors

  • Muhammad Farhan Mufid Kusuma Department of Civil and Environmental Engineering, Universitas Gadjah Mada, Yogyakarta 55281
  • Akhmad Aminullah Department of Civil and Environmental Engineering, Universitas Gadjah Mada, Yogyakarta 55281
  • Djoko Sulistyo Department of Civil and Environmental Engineering, Universitas Gadjah Mada, Yogyakarta 55281

DOI:

https://doi.org/10.21831/inersia.v19i1.54210

Keywords:

LiDAR Scanner, Point Cloud, Surface 3D, Library Python, Script Code

Abstract

The need for technological developments is needed to facilitate performance, accuracy, and effectiveness of work, especially in the field of civil engineering, is needed. With the emergence of innovative LiDAR (Light Detection and Ranging) technology scanners that are popularly used for 3D printing, developed into LiDAR Scanners for real field scanning. The result of using a LiDAR Scanner is in the form of point cloud data in a certain format, with a large enough memory. The purpose of this research is to use field point cloud data as 3D BIM data and then form a cross-section of the object. For this purpose, a special program is needed that functions to process cloud point data complexly, and is easy to use to change the shape of cloud point data to 3D data surface and 2D cross sections. The method used in this study is by creating a special program to process data point clouds using script code with the python language and several data point cloud processing libraries. In the program, 2 sub-menus will be created with certain functions: 1) Point Cloud (voxel downsampling, outlier reduction, normalize); 2) 3D model (ball pivoting/poisson surface, reduce vertex, slice mesh, transform mesh). In each data processing, the created program can only process on a specific file format; for point cloud processing in .xyz, .xyzn, .xyzrgb, .pts, .ply, .pcd formats; while for 3D data processing models are in .ply, .stl, .obj, .off , .gltf/glb format. The result of data processing using the created program can be a 3D surface with .ply /.obj format, and for cross-section generated 2D data with .jpg / .png format, and can be in the form of .dxf data for Autocad software. 3D surface data can be used as BIM data, while 2D cross-section data can be used as built 2D.

References

Mansor, H., Shukor, S. A. A., & Wong, R. (2021). An overview of object detection from building point cloud data. Journal of Physics: Conference Series, 1878(1).

https://doi.org/10.1088/1742-6596/1878/1/012058.

Daria, Kosavchenko (2020). BIM Geometry Creation from Point Cloud. Thesis: Civil and Construction Engineering, LAB University of Applied Science.

Jayakumari, R., Nidamanuri, R. R., & Ramiya, A. M. (2021). Object-level classification of vegetable crops in 3D LiDAR point cloud using deep learning convolutional neural networks. Precision Agriculture, 22(5), 1617-1633.

https://doi.org/10.1007/s11119-021-09803-0

Arastounia, M., & Lichti, D. D. (2021). Simultaneous identification, modeling and registration refinement of poles using laser scanning point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 181, 327-344.

https://doi.org/10.1016/j.isprsjprs.2021.09.017

Benedek, C., Majdik, A., Nagy, B., Rozsa, Z., & Sziranyi, T. (2021). Positioning and perception in LIDAR point clouds. Digital Signal Processing, 119, 103193.

https://doi.org/10.1016/j.dsp.2021.103193

Jonas, J. (2017). 3D Smart Sensor.

Liu, H., Zhang, Y., Lei, L., Xie, H., Li, Y., & Sun, S. (2020). Hierarchical Optimization of 3D Point Cloud Registration. Sensors, 20(23), 1-20.

https://doi.org/10.3390/s20236999

Efendy, Z. (2018). Normalisasi dalam desain database. Jurnal CoreIT, 4(1), 37-38.

Ma, W., & Li, Q. (2019). An improved ball pivot algorithm-based ground filtering mechanism for LiDAR data. Remote sensing, 11(10), 1179.

https://doi.org/10.3390/rs11101179

Schroeder, W. J., Zarge, J. A., & Lorensen, W. E. (1992, July). Decimation of triangle meshes. In Proceedings of the 19th annual conference on Computer graphics and interactive techniques (pp. 65-70).

https://doi.org/10.1145/133994.134010

King, B., Rennie, A., & Bennett, G. (2021). An efficient triangle mesh slicing algorithm for all topologies in additive manufacturing. The International Journal of Advanced Manufacturing Technology, 112(3), 1023-1033.

https://doi.org/10.1007/s00170-020-06396-2/Published

Tomaszcb, 20 November 2019, Bambo House 3D Model, accessed on 14 Oktober 2019, https://free3d.com/3d-model/bambo-house-47896.html

Kļava, Kristaps, 21 May 2021, Sample of Demo Data, accessed on 31 August 2022, http://www.merko.lv/en/demo-data

Gerharld3D, 29 November 2019, GameReady Cottage 3D Model, accessed on 21 March 2021, https://free3d.com/3d-model/gameready-cottage-free-163528.html

Downloads

Published

2023-05-31

How to Cite

Mufid Kusuma, M. F., Aminullah, A., & Sulistyo, D. (2023). Development of Cloud Point Data Processing Program for 3D BIM and 2D Cross Section Needs. Inersia : Jurnal Teknik Sipil Dan Arsitektur, 19(1), 123–132. https://doi.org/10.21831/inersia.v19i1.54210

Issue

Section

Articles