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• A Digital Cultural Heritage dataset of synthetic vaults exported as point cloud, based on shape grammar discussion. • A vaults synthetic dataset creation method for automatically generating training data when real 3D objects are insufficient. • A Deep Learning system able to segment and distinguish types of architectural vaults according to standard classifications. Historical heritage is demanding robust pipelines for preserving, enhancing, and disseminating its prominent value. Semantic segmentation of 3D Point Clouds has gained increasing attention over the years, since it might assist in automati-cally recognizing historical architectural elements, thus facilitating large dataset management. Nonetheless, semantic segmentation is particularly challenging in Cultural Heritage (CH) domain, due to the shapes complexity and the limited repeatability of elements across different architectures, which strengthens the difficulty to define common patterns within the same class of elements. Besides, as Deep Neural Networks demand an appreciably amount of labelled data to be trained, the lack of available annotated heritage point clouds prevent the research in this direction. To tackle these issues, in this paper it is proposed a Deep Learning system able to recognize historical building elements by lever-aging synthetic point cloud. The generation of the 3D models, vaults, is based on a procedural modeling approach that follows the ideal shapes, according to the rules of descriptive geometry for the main types of vaults. The approach has been applied to a newly synthetic dataset which is publicly available. This dataset comprises 6 labelled points clouds, derived from a comprehensive on-tological taxonomy in order to describe an univocal and robust architectural hierarchy: barrel vaults, groined vaults, mirror vaults, barrel vaults with clois-ter heads and lunettes, barrel vaults with lunettes, sail vaults. The experiments yield high accuracy, demonstrating the effectiveness and suitability of the pro-posed approach. [ABSTRACT FROM AUTHOR]

Unlike 2D images that are represented in regular grids, 3D point clouds are irregular and unordered, hence directly applying convolution neural networks (CNNs) to process point clouds is quite challenging. In this paper, we propose a novel deep neural network named AKNet to achieve point cloud semantic segmentation. The key to our AKNet is the attentive kernel convolution (AKConv), which is a deformed convolution operation for perceiving sufficient local context of 3D scenes. AKConv first constructs the Basic Weight Units that are robust to point's ordering. Then, for capturing the more distinctive local features, the convolution kernels of AKConv are associated with Attentive Weight Units through the self-attentive function acting on Basic Weight Units. Furthermore, 3D point clouds provide richer geometric shape information, which is helpful to recognize objects. However, inputting only raw point features to the convolution function could cause geometric information loss. Thus, we utilize augmented features as input of AKConv. Besides, to preserve the semantic information from the encoding to decoding layers, we introduce the backward encoding (BE) mechanism by utilizing higher-layer semantic features. We conduct experiments on three large-scale point clouds datasets. The experimental results demonstrate that our AKNet outperforms state-of-the-art (SOTA) networks. [ABSTRACT FROM AUTHOR]