In the cultural heritage field, point clouds, as important raw data of geomatics, are not only three-dimensional (3D) spatial presentations of 3D objects but they also have the potential to gradually advance towards an intelligent data structure with scene understanding, autonomous cognition, and a decision-making ability. The approach of point cloud semantic segmentation as a preliminary stage can help to realize this advancement. With the demand for semantic comprehensibility of point cloud data and the widespread application of machine learning and deep learning approaches in point cloud semantic segmentation, there is a need for a comprehensive literature review covering the topics from the point cloud data acquisition to semantic segmentation algorithms with application strategies in cultural heritage. This paper first reviews the current trends of acquiring point cloud data of cultural heritage from a single platform with multiple sensors and multi-platform collaborative data fusion. Then, the point cloud semantic segmentation algorithms are discussed with their advantages, disadvantages, and specific applications in the cultural heritage field. These algorithms include region growing, model fitting, unsupervised clustering, supervised machine learning, and deep learning. In addition, we summarized the public benchmark point cloud datasets related to cultural heritage. Finally, the problems and constructive development trends of 3D point cloud semantic segmentation in the cultural heritage field are presented.

Because of the age of relics and the lack of historical data, the geometric forms of missing parts can only be judged by the subjective experience of repair personnel, which leads to varying restoration effects when the geometric structure of the complex relic is reconstructed. Therefore, virtual repair effects cannot fully reflect the historical appearance of cultural relics. In order to solve this problem, this paper presents a virtual restoration method based on the multiscale spatial geometric features of cultural relics in the case of complex construction where the geometric shape of the damaged area is unknown, using the Dazu Thousand-Hand Bodhisattva statue in China as an example. In this study, the global geometric features of the three-dimensional (3D) model are analyzed in space to determine the geometric shape of the damaged parts of cultural relics. The local geometric features are represented by skeleton lines based on regression analysis, and a geometric size prediction model of the defective parts is established, which is used to calculate the geometric dimensions of the missing parts. Finally, 3D surface reconstruction technology is used to quantitate virtual restoration of the defective parts. This method not only provides a new idea for the virtual restoration of artifacts with complex geometric structure, but also may play a vital role in the protection of cultural relics.

Hyperspectral imaging technology is a research hotspot in the field of cultural heritage protection. It can be used to quickly and noninvasively obtain detailed spectral information from the surfaces of cultural relics of different categories. We can intuitively analyse pigment compositions, line characteristics, painting skills and patterns using spectral information. Hyperspectral imaging has high scientific significance and application value for the protection, restoration and research of ancient murals and other cultural relics. In this study, a mural from Daheitian hall in the Qutan temple, Qinghai Province, China, was used as a sample. The hyperspectral data were acquired and analysed for several purposes. Pigment spectral matching and abundance inversion were carried out to obtain the pigment distribution. These data were enhanced by continuum removal and histogram stretching to obtain hidden information. The dark channel prior, Criminisi and Retinex methods were used to virtually restore the image of the mural. The results indicated that by using hyperspectral imaging data, the constructed pure pigment spectrum library and suitable approaches, the types and distributions of mural pigments can be quantitatively analysed, and the lines in murals can be extracted. Hyperspectral images are helpful for identifying information hidden by pigments or surface materials. Mural images can be enhanced, and hidden information can be highlighted using enhancement methods, such as continuum removal and histogram linear stretching. In addition, hyperspectral imaging data have unique advantages in the restoration of mural images, and the combination of defogging methods and image inpainting algorithms can realize the virtual restoration of mural images. In brief, hyperspectral imaging technology was found to have a highly favourable effect on pigment analysis, line extraction, information enhancement, hidden information extraction and the virtual restoration of ancient murals.

Environmental changes and human activities can cause serious degradation of murals, where sootiness is one of the most common problems of ancient Chinese indoor murals. In order to improve the visual quality of the murals, a restoration method is proposed for sootiness murals based on dark channel prior and Retinex by bilateral filter using hyperspectral imaging technology. First, radiometric correction and denoising through band clipping and minimum noise fraction rotation forward and inverse transform were applied to the hyperspectral data of the sootiness mural to produce its denoised reflectance image. Second, a near-infrared band was selected from the reflectance image and combined with the green and blue visible bands to produce a pseudo color image for the subsequent sootiness removal processing. The near-infrared band is selected because it is better penetrating the sootiness layer to a certain extent comparing to other bands. Third, the sootiness covered on the pseudo color image was preliminarily removed by using the method of dark channel prior and by adjusting the brightness of the image. Finally, the Retinex by bilateral filter was performed on the image to get the final restored image, where the sootiness was removed. The results show that the images restored by the proposed method are superior in variance, average gradient, information entropy and gray scale contrast comparing to the results from the traditional methods of homomorphic filtering and Gaussian stretching. The results also show the highest score in comprehensive evaluation of edges, hue and structure; thus, the method proposed can support more potential studies or sootiness removal in real mural paintings with more detailed information. The method proposed shows strong evidence that it can effectively reduce the influence of sootiness on the moral images with more details that can reveal the original appearance of the mural and improve its visual quality.

Building-information-modeling for cultural heritage (HBIM), which is established using surveying data, can be used to conserve architectural heritage. The development of an HBIM model for ancient wooden architecture (AWA) structures requires interdisciplinary integration. A parametric model for the main components that intelligently integrates the historical knowledge, as well as an intelligent modeling method for these components, are two critical issues required to bridge the existing gap and improve the application of HBIM. Taking an AWA structure constructed during the Liao and Song Dynasties as an example, the parametric model for the typical components, with emphasis on commonality and characteristics, were first proposed. Subsequently, an intelligent automated modeling method was developed and programmed using Dynamo, which can intelligently identify the component type and determine the invisible dimensions. A complicated dou-gong was successfully established with surveying data using the proposed method within five minutes, thereby validating the reliability and efficiency of this method. Furthermore, the proposed method was used to establish the HBIM model of Yingxian Wood Pagoda, which is the oldest and tallest AWA structure in China with a height of 65.88 m. The research findings will provide an essential reference for the conservation of wooden architectural heritage structures.