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.

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)

We address the problem of automatic recognition of functional parts of man-made 3D shapes in the presence of significant geometric and topological variations. We observe that under such challenging circumstances, the context of a part within a 3D shape provides important cues for learning the semantics of shapes. We propose to model the context as structural relationships between shape parts and use them, in addition to part geometry, as cues for functionality recognition. We represent a 3D shape as a graph interconnecting parts that share some spatial relationships. We model the context of a shape part as walks in the graph. Similarity between shape parts can then be defined as the similarity between their contexts, which in turn can be efficiently computed using graph kernels. This formulation enables us to: (1) find part-wise semantic correspondences between 3D shapes in a nonsupervised manner and without relying on user-specified textual tags, and (2) design classifiers that learn in a supervised manner the functionality of the shape components. We specifically show that the performance of the proposed context-aware similarity measure in finding part-wise correspondences outperforms geometry-only-based techniques and that contextual analysis is effective in dealing with shapes exhibiting large geometric and topological variations.

The geometric shape of molecular surfaces strongly influences the docking processes where, although electrostatic, hydrophobic and van der Waals interactions affect greatly the binding affinity of the molecules, shape complementarity is a necessary condition. The vast majority of molecular docking algorithms uses a brute force enumeration of the transformation space, which requires extremely long running times. Few other methods use local shape feature matching to reduce the search to those relative positions which satisfy geometric constraints. Based on a shape analysis tool developed in Computer Graphics, in this paper we introduce ProTailor, a parallel algorithm for efficient multi-scale detection of morphological features on molecular surfaces. Thanks to an almost linear speed-up, we show how ProTailor is well suited to efficiently identify salient features like cavities and depressions, saddle areas or bridges. Feature identification may serve as a powerful tool to automatically locate potential binding sites or as a pre-processing step for efficient shape complementarity assessment in docking prediction.

Abstract Given a 3D solid model S represented by a tetrahedral mesh, we describe a novel algorithm to compute a hierarchy of convex polyhedra that tightly enclose S. The hierarchy can be browsed at interactive speed on a modern PC and it is useful for implementing an intuitive feature selection paradigm for 3D editing environments. Convex parts often coincide with perceptually relevant shape components and, for their identification, existing methods rely on the boundary surface only. In contrast, we show that the notion of part concavity can be expressed and implemented more intuitively and efficiently by exploiting a tetrahedrization of the shape volume. The method proposed is completely automatic, and generates a tree of convex polyhedra in which the root is the convex hull of the whole shape, and the leaves are the tetrahedra of the input mesh. The algorithm proceeds bottom‐up by hierarchically clustering tetrahedra into nearly convex aggregations, and the whole process is significantly fast. We prove that, in the average case, for a mesh of n tetrahedra O(n log2n) operations are sufficient to compute the whole tree.

In this paper we propose a method for the automatic extraction and annotation of the animation control skeleton of virtual humans, which relies on an a-priori knowledge of the human anatomy. The method is based on a segmentation of the virtual human shape into semantically meaningful features, like arms or legs, and on an automatic location and labeling of joints of the control skeleton. The method is particularly relevant for computer animation where the process still largely relies on manual tasks, and especially for virtual characters built on real scanned data. Several examples will show the results obtained with our approach.

Mesh segmentation has become an important component in many applications in computer graphics. In the last several years, many algorithms have been proposed in this growing area, offering a diversity of methods and various evaluation criteria. This paper provides a comparative study of some of the latest algorithms and results, along several axes. We evaluate only algorithms whose code is available to us, and thus it is not a comprehensive study. Yet, it sheds some light on the vital properties of the methods and on the challenges that future algorithms should face

The paper investigates the main issues related to the definition of abstraction tools for deriving high-level descriptions of complex geometric models. Among the wide range of shape descriptors, topological graph-like representations not only give a powerful and synthetic sketch of the object, but also capture its inner structure, that is how features connect together to give the overall shape. This aspect makes them useful to describe complex 3D objects in various applications like modeling, morphing, matching and recognition. The paper surveys the main properties of skeletons developed in shape modeling for representing objects.

Different application fields have shown increasing interest in shape description oriented to recognition and similarity issues. Beyond the application aims, the capability of handling details separating them from building elements, the invariance to a set of geometric transformations, the uniqueness and stability to noise represent fundamental properties of each proposed model. This paper defines an affine-invariant skeletal representation; starting from global features of a 3D shape, located by curvature properties, a Reeb graph is defined using the topological distance as a quotient function. If the mesh has uniformly spaced vertices, this Reeb graph can also be rendered as a geometric skeleton defined by the barycenters of pseudo-geodesic circles sequentially expanded from all the feature points.