Surface Reconstruction from Unstructured Point Cloud Data for Building Digital Twin

This study highlights on the methods used for surface reconstruction from unstructured point cloud data, characterized by simplicity, robustness and broad applicability from 3D point cloud data. The input data consists of unstructured 3D point cloud data representing a building. The reconstruction methods tested here are Poisson Reconstruction Algorithm, Ball Pivoting Algorithm, Alpha Shape Algorithm and 3D surface refinement, employing mesh refinement through Laplacian smoothing and Simple Smoothing techniques. Analysis on the algorithm parameters and their influence on reconstruction quality, as well as their impact on computational time are discussed. The findings offer valuable insights into parameter behavior and its effects on computational efficiency and level of detail in the reconstruction process, contributing to enhanced 3D modeling and digital twin for buildings.

@article{Ismail2023,
  title     = {Surface Reconstruction from Unstructured Point Cloud Data for Building Digital Twin},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {10},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {F. A. Ismail and S.A. Abdul Shukor and N.A. Rahim and R. Wong},
  doi       = {10.14569/IJACSA.2023.0141075},
  url       = {https://doi.org/10.14569/IJACSA.2023.0141075}
}