3D Human Face Surface Reconstruction from Unstructured Point Cloud Face Images

Zuraimi, Muhammad Azhad Bin; Zaman, Fadhlan Hafizhelmi Kamaru; Mazalan, Lucyantie

doi:10.1109/icsgrc55096.2022.9845175

Cited by 1 publication

(1 citation statement)

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“…To extract the 3D file format from the .ptx file and transform it into a commonly used 3D point cloud data format, such as the .pcd file format, a comprehensive understanding of the binary file data structure assumes paramount significance. Each .ptx file encompasses vital data elements, encompassing color information, the minimum depth value (zmin), the subsampled number of rows (nrows), the subsampled number of columns (ncols), the image file name, and an Nx5 matrix signifying 3D and 2D normalized coordinates falling within the [0,1] range [19]. Herein, N denotes the product of nrows and ncols, where values equivalent to zmin denote the background.…”

Section: A the Sample Datamentioning

confidence: 99%

Surface Reconstruction from Unstructured Point Cloud Data for Building Digital Twin

Ismail,

Shukor,

Rahim

et al. 2023

IJACSA

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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.

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Section: A the Sample Datamentioning

confidence: 99%