AN EXTRACTION METHOD FOR ROOF POINT CLOUD OF ANCIENT BUILDING USING DEEP LEARNING FRAMEWORK
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.