Segmentation and visualization of three-dimensional digital cultural heritage are important analytical tools for the intuitive understanding of content. In this paper, we propose a semantic segmentation and visualization framework that automatically classifies carved items (people, buildings, plants, etc.) in cultural heritage reliefs. We also apply our method to the bas-reliefs of Borobudur Temple, a UNESCO World Heritage Site in Indonesia. The difficulty in relief segmentation lies in the fact that the boundaries of each carved item are formed by indistinct soft edges, i.e., edges with low curvature. This unfavorable relief feature leads the conventional methods to fail to extract soft edges, whether they are three-dimensional methods classifying a three-dimensional scanned point cloud or two-dimensional methods classifying pixels in a drawn image. To solve this problem, we propose a deep-learning-based soft edge enhanced network to extract the semantic labels of each carved item from multichannel images that are projected from the three-dimensional point clouds of the reliefs. The soft edges in the reliefs can be clearly extracted using our novel opacity-based edge highlighting method. By mapping the extracted semantic labels into three-dimensional points of the relief data, the proposed method provides comprehensive three-dimensional semantic segmentation results of the Borobudur reliefs.

The preservation and analysis of tangible cultural heritage sites have attracted enormous interest worldwide. Recently, establishing three-dimensional (3D) digital archives has emerged as a critical strategy for the permanent preservation and digital analysis of cultural sites. For extant parts of cultural sites, 3D scanning is widely used for efficient and accurate digitization. However, in many historical sites, many parts that have been damaged or lost by natural or artificial disasters are unavailable for 3D scanning. The remaining available data sources for these destroyed parts are photos, computer-aided design (CAD) drawings, written descriptions, etc. In this paper, we achieve an integrated digital archive of a UNESCO World Heritage site, namely, the Borobudur temple, in which buried reliefs and internal foundations are not available for 3D scanning. We introduce a digitizing framework to integrate three different kinds of data sources and to create a unified point-cloud-type digital archive. This point-based integration enables us to digitally record the entire 3D structure of the target cultural heritage site. Then, the whole site is visualized by stochastic point-based rendering (SPBR) precisely and comprehensibly. The proposed framework is widely applicable to other large-scale cultural sites.

The digital documentation and analysis of cultural heritage increasingly rely on high-precision three-dimensional point cloud data, which often suffers from missing regions due to limitations in acquisition conditions, hindering subsequent analyses and applications. Point cloud completion techniques, by predicting and filling these missing regions, are vital for restoring the integrity of cultural heritage structures, enhancing restoration accuracy and efficiency. In this paper, for challenges in processing large-scale cultural heritage point clouds, particularly the slow processing speed and visualization impairments from uneven point density during completion, we propose a point cloud completion employing centroid-based voxel feature extraction, which significantly accelerates feature extraction for massive point clouds. Coupled with an efficient upsampling module, it achieves a uniform point distribution. Experimental results show that the proposed method matches SOTA performance in completion accuracy while surpassing in point density uniformity, demonstrating capability in handling larger-scale point cloud data, and accelerating the processing of voluminous point clouds. In general, the proposed method markedly enhances the efficiency and quality of large-scale point cloud completion, holding significant value for the digital preservation and restoration of cultural heritage.

The Nanhai No. 1 shipwreck is an ancient wooden ship in the Southern Song Dynasty. Currently, serious challenges of microbial diseases exist on the hull wood. This study aimed to obtain microbial samples from the ship hull in December 2021 and analyze the microbial diseases through scanning electron microscopy and high-throughput sequencing to preserve the Nanhai No. 1 shipwreck. The biodegradation mechanism of diseased microorganisms was explored through whole genome sequencing and the detection of enzyme activity and gene expression levels of diseased microorganisms under different conditions. The results showed that there was obvious fungal colonization on the surface of the hull wood and Fusarium solani NK-NH1 was the dominant disease fungus on the surface. NK-NH1 has strong cellulose and lignin degradation ability. Its whole genome size is 52,389,955 bp, and it contains 17,402 genes. It has a variety of key enzyme genes involved in cellulose and lignin degradation. The NK-NH1 dominant degrading enzyme lignin peroxidase has the highest enzyme activity at pH = 4, NaCl concentration of 30%, and FeSO₄ concentration of 50 mg/L, while laccase has the highest enzyme activity at pH = 4, NaCl concentration of 10%, and FeSO₄ concentration of 100 mg/L. The above research results prove that NK-NH1 is a key fungus to the biodegradation of ship hull wood when it is exposed to air, low pH, high salt, and rich in sulfur iron compounds. This study provides a theoretical basis for the preservation of the Nanhai No. 1 shipwreck.

Recent advances in 3D scanning have enabled the digital recording of complex objects as large-scale point clouds, which require clear visualization to convey their 3D shapes effectively. Edge-highlighting visualization is used to improve the comprehensibility of complex 3D structures by enhancing the 3D edges and high-curvature regions of the scanned objects. However, traditional methods often struggle with real-world objects due to inadequate representation of soft edges (i.e., rounded edges) and excessive line clutter, impairing resolution and depth perception. To address these challenges, we propose a novel visualization method for 3D scanned point clouds based on dual 3D edge extraction and opacity–color gradation. Dual 3D edge extraction separately identifies sharp and soft edges, integrating both into the visualization. Opacity–color gradation enhances the clarity of fine structures within soft edges through variations in color and opacity, while also creating a halo effect that improves both resolution and depth perception of the visualized edges. Computation times required for dual 3D edge extraction are comparable to conventional binary statistical edge-extraction methods. Visualizations with opacity–color gradation are executable at interactive rendering speeds. The effectiveness of the proposed method is demonstrated using 3D scanned point cloud data from high-value cultural heritage objects.

Digital archives that support the three-dimensional (3D) digitization of cultural heritage sites are gaining popularity and attracting enormous interest as a critical strategy for digital preservation. This paper investigates the construction of a multimodal database for cultural heritage sites, which aims to preserve 3D data, images, videos and other materials along with multilingual text data. 3D scanning is widely used for efficient and accurate digitization of cultural heritages. However, some sites have been damaged entirely or partially, or sometimes, it is impossible to conduct 3D scanning of a portion of a site that is hidden from sight. At the Borobudur temple, a UNESCO World Heritage site, there are buried reliefs that are hidden at the basement and inaccessible for 3D scanning. Currently available data for these inaccessible portions of the site include old photos, computer-aided design (CAD) drawings and historical written descriptions. To digitally reconstruct 3D models of hidden parts, we introduce a method that use a single monocular photo. We constructed “Borobudur Visual Archives” to store, access, view and retrieve Borobudur-related digital assets, including the 3D data that we created.