Deep Contour Detection for Enhanced Heritage Sites Visualization by Laser Point Clouds

Abstract. Laser scanning is a key tool for capturing detailed 3D representations of cultural heritage sites, enabling applications in visualization, conservation, and analysis. However, the resulting point clouds comprise of visually monotonous surfaces with little intuitive value for direct interpretation. Conventional representations—such as collections of surfaces, 3D lines, or boundary-based topological models—often lack the expressiveness or adaptability needed to capture semantically rich, human-intuitive features like salient architectural regions. These salient regions serve as interpretable medium to understand the scan content but remain difficult to extract. Therefore, this paper proposes a framework that learns salient region-aware representations from raw point clouds. Central to the proposed approach is a heat-propagation-driven graph network trained with a new spectral supervision signal. To ensure scalability, our network operates on local patches and introduces a prediction aggregation scheme that efficiently scales to heritage-scale scenes with millions of points. Results show superior performance over existing salient region depiction approaches, producing high-quality results in under two seconds. It is orders of magnitude faster than state-of-the-art methods, even for large-scale noisy terrestrial scans of millions of points.