Assessing Generalization Capability of 3D Semantic Segmentation Algorithms using 3D Point Clouds of Cultural Heritage

Cultural Heritage (CH) monuments are strongly characterized by detailed architectural elements, inherent complexity, and heterogeneity and therefore present unique challenges regarding 3D Semantic Segmentation (3DSS), which is a useful tool for documentation enhancement and for empowering preservation actions. This study explores the generalization capability of recent deep learning 3DSS architectures applied to cultural heritage (CH) point cloud data. Using the ArCH benchmark, we evaluate five representative models, including PointNet, PointNet++, Point Transformer v1, v2 and Omni-Adaptive CNNs. All models are assessed using a uniform pipeline and limited input features (XYZ and RGB). Both qualitative and quantitative results indicate that Point Transformer v1 achieves strong performance on unseen CH data (61.3 mIoU), suggesting a potential link between architectural design and generalization ability in CH domain. These findings highlighting the need for further research under varying configurations and broader evaluation settings, especially for recent deep learning architecturs e.g., transformers.