Airborne point clouds provide a rich but unstructured description of urban environments, making their direct use in visualization, simulation, and geospatial applications difficult in non-trivial use-cases. This paper presents CityLODer , a automatic and scalable pipeline for converting raw data into usable 3D city representations with a unified procedure. Building on the earlier LiD2LOD system, CityLODer broadens the reconstruction process to a more comprehensive urban representation that includes both buildings and roads. The tool produces semantic CityGML Level of Detail 1 models, together with lightweight triangular meshes. Particular attention is given to robustness, ease of use, and automation, allowing the pipeline to process large airborne datasets with limited manual intervention. We demonstrate the system on historical urban scenarios characterized by irregular layouts and complex morphology, showing that CityLODer can generate compact, semantically structured, and visually effective city models from raw point-cloud data.

Unlike image or text domains that benefit from an abundance of large-scale datasets, point cloud learning techniques frequently encounter limitations due to the scarcity of extensive datasets. To overcome this limitation, we present Symmetria, a formula-driven dataset that can be generated at any arbitrary scale. By construction, it ensures the absolute availability of precise ground truth, promotes data-efficient experimentation by requiring fewer samples, enables broad generalization across diverse geometric settings, and offers easy extensibility to new tasks and modalities. Using the concept of symmetry, we create shapes with known structure and high variability, enabling neural networks to learn point cloud features effectively. Our results demonstrate that this dataset is highly effective for point cloud self-supervised pre-training, yielding models with strong performance in downstream tasks such as classification and segmentation, which also show good few-shot learning capabilities. Additionally, our dataset can support fine-tuning models to classify real-world objects, highlighting our approach’s practical utility and application. We also introduce a challenging task for symmetry detection and provide a benchmark for baseline comparisons. A significant advantage of our approach is the public availability of the dataset, the accompanying code, and the ability to generate very large collections, promoting further research and innovation in point cloud learning.

The identification and recognition of urban features are essential for creating accurate and comprehensive digital representations of cities. In particular, the automatic characterization of façade elements plays a key role in enabling semantic enrichment and 3D reconstruction. It also supports urban analysis and underpins various applications, including planning, simulation, and visualization. This work presents a pipeline for the automatic recognition of façades within complex urban scenes represented as point clouds. The method employs an enhanced partitioning strategy that extends beyond strict building footprints by incorporating surrounding buffer zones, allowing for a more complete capture of façade geometry, particularly in dense urban contexts. This is combined with a primitive recognition stage based on the Hough transform, enabling the detection of both planar and curved façade structures. The proposed partitioning overcomes the limitations of traditional footprint-based segmentation, which often disregards contextual geometry and leads to misclassifications at building boundaries. Integrated with the primitive recognition step, the resulting pipeline is robust to noise and incomplete data, and supports geometry-aware façade recognition, contributing to scalable analysis of large-scale urban environments.

Aerial LiDAR (and photogrammetric) surveys are becoming a common practice in land and urban management, and aerial point clouds (or the reconstructed surfaces) are increasingly used as digital representations of natural and built structures for the monitoring and simulation of urban processes or the generation of what-if scenarios. The geometric analysis of a “digital twin” of the built environment can contribute to provide quantitative evidence to support urban policies like planning of interventions and incentives for the transition to renewable energy. In this work, we present a geometry-based approach to efficiently and accurately estimate the photovoltaic (PV) energy produced by urban roofs. The method combines a primitive fitting technique for detecting and characterizing building roof components from aerial LiDAR data with an optimization strategy to determine the maximum number and optimal placement of PV modules on each roof surface. The energy production of the PV system on each building over a specified time period (e.g., one year) is estimated based on the solar radiation received by each PV module and the shadow projected by neighboring buildings or trees and efficiency requirements. The strength of the proposed approach is its ability to combine computational techniques, domain expertise, and heterogeneous data into a logical and automated workflow, whose effectiveness is evaluated and tested on a large-scale, real-world urban areas with complex morphology in Italy.

Given a 3D point cloud, we propose a method for suitably resampling the cloud while reconstructing and preserving the feature curves to which some points are identified to belong. The first phase of our strategy enriches the cloud by approximating the curvilinear profiles outlined by the feature points with piece-wise polynomial parametric space curves through the use of the Hough transform. The second phase describes how the removal of a point or its insertion can be performed without affecting the approximated profiles and preserving the enriched structure of the cloud. The combination of the two steps provides multiple possibilities for processing a point cloud by varying its size or improving its density homogeneity without affecting the retrieved feature curves. The various capabilities of our approach are investigated to produce simplification, refinement, and resampling techniques whose effectiveness is evaluated through experiments and comparisons.

Feature curves are space curves identified by color or curvature variations in a shape, which are crucial for human perception (Biederman, 1995). Detecting these characteristic lines in 3D digital models becomes important for recognition and representation processes. For recognizing plane curves in images, the Hough transform (HT) provided a very good solution to the problem. It selects the best-fitting curve in a dictionary of families of curves through a voting procedure that makes it robust to noise and missing parts. Since 3D digital models are often obtained by scanning real objects and may have many defects, the HT has been extended to recognize and approximate space curves in 3D models that correspond to relevant features This work overviews three HT-based different approaches for identifying and approximating spatial profiles of points extracted from point clouds or meshes. A first attempt at this extension involved projecting the spatial points onto the regression plane, thus reducing the problem to planar recognition and using families of plane curves. A second approach has been proposed to recognize spatial profiles that cannot be projected onto the regression plane, using two types of space curve families. Unfortunately, the main drawback of methods based on traditional HT is that it requires prior knowledge of which family of curves to look for. To overcome this limitation, a third method has been developed that provides a piecewise space curve approximation using specific parametric polynomial curves. Additionally, free-form curves that a parametric or implicit form cannot express can be represented using this technique. In the paper, we also analyze the pros and cons of the various approaches and how they managed and reduced the HT's computational cost, given the large number of parameters introduced when families of space curves are considered.

Urban simulations that involve disaster prevention, urban design, and assisted navigation heavily rely on urban geometric models. While large urban areas need a lot of time to be acquired terrestrially, government organizations have already conducted massive aerial LiDAR surveys, some even at the national level. This work aims to provide a pipeline for extracting multi-scale point clouds from 2D building footprints and airborne LiDAR data, which depends on whether the points represent buildings, vegetation, or ground. We denoise the roof slopes, match the vegetation, and roughly recreate the building façades frequently hidden to aerial acquisition using a parametric representation of geometric primitives. We then carry out multiple-scale samplings of the urban geometry until a 3D urban representation can be achieved because we annotate the new version of the original point cloud with the parametric equations representing each part. We mainly tested our methodology in a real-world setting – the city of Genoa – which includes historical buildings and is heavily characterized by irregular ground slopes. Moreover, we present the results of urban reconstruction on part of two other cities, Matera, which has a complex morphology like Genoa, and Rotterdam.

Modelling objects at a large resolution or scale brings challenges in the storage and processing of data and requires efficient structures. In the context of modelling urban environments, we face both issues: 3D data from acquisition extends at geographic scale, and digitization of buildings of historical value can be particularly dense. Therefore, it is crucial to exploit the point cloud derived from acquisition as much as possible, before (or alongside) deriving other representations (e.g., surface or volume meshes) for further needs (e.g., visualization, simulation). In this paper, we present our work in processing 3D data of urban areas towards the generation of a semantic model for a city digital twin. Specifically, we focus on the recognition of shape primitives (e.g., planes, cylinders, spheres) in point clouds representing urban scenes, with the main application being the semantic segmentation into walls, roofs, streets, domes, vaults, arches, and so on. Here, we extend the conference contribution in Romanengo et al. (2023a), where we presented our preliminary results on single buildings. In this extended version, we generalize the approach to manage whole cities by preliminarily splitting the point cloud building-wise and streamlining the pipeline. We added a thorough experimentation with a benchmark dataset from the city of Tallinn (47,000 buildings), a portion of Vaihingen (170 building) and our case studies in Catania and Matera, Italy (4 high-resolution buildings). Results show that our approach successfully deals with point clouds of considerable size, either surveyed at high resolution or covering wide areas. In both cases, it proves robust to input noise and outliers but sensitive to uneven sampling density. • Processing 3D point clouds of urban areas to generate a semantic model for a city digital twin. • Recognizing shape primitives (planes, cylinders, spheres) in large-scale urban point clouds. • Semantic segmentation of building point clouds into facades, roofs, and pavement. • Experimenting on real datasets (Vaihingen, Tallinn, Catania and Matera)

Abstract We propose CurveML, a benchmark for evaluating and comparing methods for the classification and identification of plane curves represented as point sets. The dataset is composed of 520k curves, of which 280k are generated from specific families characterised by distinctive shapes, and 240k are obtained from Bézier or composite Bézier curves. The dataset was generated starting from the parametric equations of the selected curves making it easily extensible. It is split into training, validation, and test sets to make it usable by learning-based methods, and it contains curves perturbed with different kinds of point set artefacts. To evaluate the detection of curves in point sets, our benchmark includes various metrics with particular care on what concerns the classification and approximation accuracy. Finally, we provide a comprehensive set of accompanying demonstrations, showcasing curve classification, and parameter regression tasks using both ResNet-based and PointNet-based networks. These demonstrations encompass 14 experiments, with each network type comprising 7 runs: 1 for classification and 6 for regression of the 6 defining parameters of plane curves. The corresponding Jupyter notebooks with training procedures, evaluations, and pre-trained models are also included for a thorough understanding of the methodologies employed.

The problem faced in this paper concerns the recognition of simple and complex geometric primitives in point clouds resulting from scans of mechanical CAD objects. A large number of points, the presence of noise, outliers, missing or redundant parts and uneven distribution are the main problems to be addressed to meet this need. In this article we propose a solution, based on the Hough transform, that can recognize simple and complex geometric primitives and is robust to noise, outliers, and missing parts. Additionally, we can extract a series of geometric descriptors that uniquely characterize a primitive and, based on them, aggregate the output into maximal or compound primitives, thus reducing oversegmentation. The results presented in the paper demonstrate the robustness of the method and its competitiveness with respect to other solutions proposed in the literature.