Abstract: Stone cultural relics are important material carriers of civilization, and detecting their surface damage is a core task in the preventive conservation of cultural heritage.However, when dealing with point cloud data characterized by complex surface textures and varying degrees of weathering, existing detection and segmentation algorithms exhibit two limitations: 1) traditional hard-threshold methods rely on manual parameter tuning and exhibit poor adaptability; 2) artificial textures and natural weathering damage are highly similar in geometric features, which leads to region merging and confusion between them during segmentation. Therefore, an automatic detection method combining a statistical model and geometric topological analysis was proposed. First, geometric roughness and color consistency were selected to construct a two-dimensional feature vector, and unsupervised adaptive anomaly extraction was realized by a Gaussian mixture model to replace the traditional hard-threshold method relying on manual parameter tuning. Subsequently, a multi-constraint locally convex connected patches(LCCP) segmentation algorithm was designed. By introducing an orthogonal distance constraint and a normal angle constraint, it solved the region-merging problem between textures and damage from a geometric-topological perspective, and a post-processing step was conducted to optimize the segmentation results. Validation was carried out on three typical test regions with texture–damage merging. Compared with the traditional LCCP algorithm and region growing algorithm, indicators of the proposed method such as precision, F1-score, and intersection over union were comprehensively improved; the F1-scores on the three test regions reached 0.915 8, 0.906 3, and 0.926 0, respectively. The proposed method effectively resolved the merging problem between textures and damage, and provided a reliable solution for the digital detection of damage in stone cultural relics.

Key words: stone cultural relic conservation, three-dimensional point cloud, damage detection, Gaussian mixture model, locally convex connected patch