An Extraction Method for Roof Point Cloud of Ancient Building Using Deep Learning Framework

Abstract

Abstract. Chinese ancient architecture is a valuable heritage wealth, especially for roof that reflects the construction age, structural features and cultural connotation. Point cloud data, as a flexible representation with characteristics of fast, precise, non-contact, plays a crucial role in a variety of applications for ancient architectural heritage, such as 3D fine reconstruction, HBIM, disaster monitoring etc. However, there are still many limitations in data editing tasks that need to be worked out manually, which is time-consuming, labor-intensive and error-prone. In recent years, the theoretical advance on deep learning has stimulated the development of various domains, and digital heritage is not in exception. Whenever, deep learning algorithm need to consume a huge amount of labeled date to achieve the purpose for segmentation, resulting a actuality that high labor costs also be acquired. In this paper, inspired by the architectural style similarity between mimetic model and real building, we proposed a method supported by deep learning, which aims to give a solution for the point cloud automatic extraction of roof structure. Firstly, to generate real point cloud, Baoguang Temple, unmanned Aerial Vehicle (UAV) is presented to obtain image collections that are subsequently processed by reconstruction technology. Secondly, a modified Dynamic Graph Convolutional Neural Network (DGCNN) which can learn local features with taking advantage of an edge attention convolution is trained using simulated data and additional attributes of geometric attributes. The mimetic data is sampled from 3DMAX model surface. Finally, we try to extract roof structure of ancient building from real point clouds scenes utilizing the trained model. The experimental results show that the proposed method can extract the rooftop structure from real scene of Baoguang, which illustrates not only effectiveness of approach but also a fact that the simulated source perform potential value when real point cloud datasets are scarce.

Machine and Deep Learning Implementations for Heritage Building Information Modelling: A Critical Review of Theoretical and Applied Research

“…Many criticise the initial HBIM geometry generation step from the captured point cloud as a bottleneck problem and underline the need for its automation [5], [6], [7]. Inspired by autonomous driving, computer-aided manufacturing and surveying sectors that also utilise point clouds, automated classification methods have been the most common hypothesis for addressing the articulated challenge.…”

Eei-Net: Edge-Enhanced Interpolation Network for Semantic Segmentation of Historical Building Point Clouds

“…More semantic segmentation methods based on deep learning have also been proposed in the field of historical architectural heritage. Dong et al (Ji et al, 2021) modified DGCNN to segment MQDOA roofs. Francesca Matrone et al compared machine learning methods with deep learning methods for large 3D artifact classification, synthesized the advantages of both methods, and proposed a cultural heritage point cloud semantic segmentation architecture, DGCNN-Mod+3Dfeat, that incorporates the advantages of both methods.…”

A Review of Point Cloud Segmentation of Architectural Cultural Heritage

“…, and at the same time can well balance the accuracy and complexity of the algorithm, providing a new idea for point cloud segmentation of architectural cultural heritage.Gunes et al (Gunes et al, 2022) used parametric definitions of historic building elements to generate a semi-automatic synthetic dataset, the Historic Dome Dataset (HDD). Based on the similarity of Chinese ancient architectural styles, Ji et al(Ji Y et al, 2021) used 3DMAX model to train sampling points and extracted complex roof point clouds from real ancient buildings. Although synthetic point clouds are more regular and complete than real scanned point clouds, they are able to save the cost of manually labeling training data, have structural similarity to real scanned point clouds, and are important for improving the generalization ability of neural networks.…”