Conference Agenda

To enable non-destructive quantitative identification of mixed salts in mural plaster layers, hyperspectral data were collected from Na₂SO₄-CaCl₂ mixed-salt samples. Based on these data, a method integrating spectral preprocessing, feature-set construction, and machine-learning inversion was proposed. First, the original spectra were preprocessed using Savitzky-Golay smoothing and multiplicative scatter correction. A 0.6-order fractional-order derivative (FOD) was then introduced to enhance subtle salt-related spectral features. Subsequently, 30 single-band features were selected using a two-step strategy involving competitive adaptive reweighted sampling for preliminary screening and variable importance in projection for secondary screening. On this basis, dual-band and tri-band spectral indices were further constructed, and a combined-band feature set was formed by integrating the three feature sets. Gaussian process regression (GPR) was used to compare the inversion performance of different feature-input strategies for Na₂SO₄ and CaCl₂ contents. The results showed that the 0.6-order FOD achieved a favorable balance between feature enhancement and noise suppression. Among the evaluated feature-input strategies, the combined-band model showed the best predictive performance for both Na₂SO₄ and CaCl₂. These results indicate that integrating complementary information from feature sets with different dimensions can improve the stability and accuracy of mixed-salt inversion, providing a useful reference for the hyperspectral non-destructive quantification of mixed salts in murals.

Map archives, serving as crucial cultural heritage documenting historical spatial information, face severe challenges in long-term preservation. To evaluate the feasibility of deacidification technology in the conservation of map archives, this study utilized 41 severely acidified early 20th-century map archives as samples. These were treated using a specific non-aqueous deacidification technology, and changes in pH value, color difference (ΔE), and inks stability before and after treatment were analyzed. The results indicate that after deacidification, the paper pH value significantly increased from an average of 4.48 to a range between 8.24 and 8.87. The color change was minimal, with an average color difference ΔE of only 1.62. This study verifies that the deacidification technology is suitable and effective for the deacidification treatment of acidified paper-based map archives, providing a safe and reliable method for preserving their cultural value.

To address the challenges of large initial pose discrepancies in grotto point clouds acquired from multiple sources, complex local geometric structures, significant noise interference, and the tendency of traditional ICP algorithms to fall into local optima, a high-precision point cloud registration method is proposed by integrating feature extraction with the collaborative optimization of coarse and fine registration. This method first performs point cloud preprocessing through voxel downsampling and outlier removal; it then extracts stable feature regions based on normal vector estimation and curvature analysis, and constructs feature representations using FPFH descriptors; building on this, the K-4PCS algorithm is employed to perform coarse registration and obtain optimal initial transformation parameters, followed by fine registration using an improved ICP algorithm combined with KD-tree-based search optimization. The proposed method was validated using the STANFORD DRAGON dataset and the point cloud of the Buddha head statue from Cave 18 of the Yungang Grottoes. The results indicate that the proposed method effectively improves the convergence speed and accuracy of point cloud registration. It demonstrates good stability and applicability in complex cave heritage scenarios and can provide methodological support for the fusion of multi-source point clouds in the digital preservation of cultural heritage.

To address the lack of suitable methods for automatic 2D line drawing generation from grotto wall mesh models, as well as the difficulty of existing methods in balancing structural representation and detail preservation, this paper proposes a line drawing generation method based on depth map and normal map fusion. The method first orthographically projects the 3D model into a depth map and a surface normal map, then constructs an initial line drawing pipeline based on projected edge fusion. A layered optimization strategy is further introduced to improve detail representation and result stability. Experiments on the mesh model of the north wall of Cave 18 at the Yungang Grottoes show that the projected edge fusion method is more suitable for overall structural representation, while the layered optimization method performs better in preserving weak structures and fine details. The proposed method effectively improves the quality of automatic 2D line drawing generation for grotto wall surfaces.

Rock-cut cave temples, such as the UNESCO World Heritage site of Yungang Grottoes, represent invaluable cultural heritage facing severe deterioration. Traditional monitoring methods are often slow, subjective, and inadequate for large-scale, long-term analysis, creating a critical gap in effective conservation.To address this challenge, we developed an automated framework for identifying surface deterioration features on stone carvings using deep learning. Our approach leverages a novel multi-source image dataset, combining historical and modern imagery of the Yungang Grottoes. We propose an enhanced model based on the YOLO architecture, featuring a synergistic semantic and spatial perception mechanism that significantly improves the detection of subtle features like peeling and cracks.The model was trained to recognize three key deterioration types: peeling, crack, and human damage. On-site deployment and testing in the authentic cave environments demonstrated excellent performance, achieving high recognition confidence for cracks (87.5%), peeling (85.2%), and human damage (81.3%). This study provides a powerful new tool for the quantitative monitoring of stone carvings, offering a scientifically-informed pathway for practical and proactive conservation strategies at heritage sites worldwide.

Most of the grotto temples have undergone long-term weathering and multiple repainting campaigns, so accurate identification of the composition and spatial abundance of surface pigments is an important foundation for pigment characterization and conservation research. This study focuses on the dome of Yungang Grotto 7. Data were acquired using a three-dimensional (3D) hyperspectral multimodal digital acquisition system and the Analytical Spectral Devices (ASD) field spectroradiometer. The workflow consisted of two stages: pigment identification and abundance inversion. In the pigment identification stage, a normalized weighted identification method integrating Spectral Angle Mapper (SAM) and the Normalized Difference Spectral Index (NDSI) was proposed based on mineral pigment reflectance curves measured by the ASD field spectroradiometer. In the abundance inversion stage, Fully Constrained Least Squares (FCLS) was applied to estimate pigment proportions in mixed pixels under non-negativity and sum-to-one constraints. The results show that the green pigments are most likely malachite and Paris green, the red pigments are most likely hematite and laterite, and the black pigment is most likely carbon black. The interwoven distribution of Paris green and traditional mineral pigments provides material-science evidence for modern repainting and restoration in this area. Nonlinear mixing may occur on rough and weathered grotto surfaces. However, under the current data conditions, its influence on abundance inversion remains unclear. Therefore, Kernel Fully Constrained Least Squares (K-FCLS) was additionally introduced as a reference nonlinear model for qualitative comparison with FCLS.