Symmetry Detection and Analysis of Chinese Paifang Using 3D Point Clouds

Abstract

The Chinese paifang is an essential constituent element for Chinese or many other oriental architectures. In this paper, a new method for detection and analysis of the reflection symmetry of the paifang based on 3D point clouds is proposed. The method invokes a new model to simultaneously fit two vertical planes of symmetry to the 3D point cloud of a paifang to support further symmetry analysis. Several simulated datasets were used to verify the proposed method. The results indicated that the proposed method was able to quantity the symmetry of a paifang in terms of the RMSE obtained from the ICP algorithm, with resistance to the presence of some random noise added to the simulated measurements. For real datasets, three old Chinese paifangs (with ages from 90 to 500 years) were scanned as point clouds to input into the proposed method. The method quantified the degree of symmetry for the three Chinese paifangs in terms of the RMSE, which ranged from 20 to 61 mm. One of the paifangs with apparent asymmetry had the highest RMSE (61 mm). Other than the quantification of the symmetry of the paifangs, the proposed method could also locate which portion of the paifang was relatively more symmetric. The proposed method can potentially be used for structural health inspection and cultural studies of the Chinese paifangs and some other similar architecture.

RANSAC-Based Planar Point Cloud Segmentation Enhanced by Normal Vector and Maximum Principal Curvature Clustering

“…Light Detection and Ranging (LiDAR, renowned for its ability to promptly generate detailed three-dimensional (3D) spatial data. With the advancement of hight-precision LiDAR sensors, an increasing array of fields are incorporating this technology, such as autonomous vehicles Ibanez-Guzman, 2020 , Zhao et al, 2020), cultural heritage management (Chan et al, 2011, Rodrí guez-Gonzá lvez et al, 2017, Teppati Losè et al, 2022, mobile robotics (Weiss andBiber, 2011 , Yang et al, 2022), and smart cities (Garnett andAdams, 2018 , Ortega et al, 2021). Three-dimensional point clouds, as a data type for recording LiDAR measurements, require extensive post-processing in which the plane segmentation is one of most significant processing operations.…”