A Survey of Methods for Converting Unstructured Data to CSG Models

The goal of this document is to survey existing methods for recovering CSG representations from unstructured data such as 3D point-clouds or polygon meshes. We review and discuss related topics such as the segmentation and fitting of the input data. We cover techniques from solid modeling and CAD for polyhedron to CSG and B-rep to CSG conversion. We look at approaches coming from program synthesis, evolutionary techniques (such as genetic programming or genetic algorithm), and deep learning methods. Finally, we conclude with a discussion of techniques for the generation of computer programs representing solids (not just CSG models) and higher-level representations (such as, for example, the ones based on sketch and extrusion or feature based operations).Comment: 29 page

Representation Learning for Shape Decomposition, By Shape Decomposition

The ability to parse 3D objects into their constituent parts is essential for humans to understand and interact with the surrounding world. Imparting this skill in machines is important for various computer graphics, computer vision, and robotics tasks. Machines endowed with this skill can better interact with its surroundings, perform shape editing, texturing, recomposing, tracking, and animation. In this thesis, we ask two questions. First, how can machines decompose 3D shapes into their fundamental parts? Second, does the ability to decompose the 3D shape into these parts help learn useful 3D shape representations? In this thesis, we focus on parsing the shape into compact representations, such as parametric surface patches and Constructive Solid Geometry (CSG) primitives, which are also widely used representations in 3D modeling in computer graphics. Inspired by the advances in neural networks for 3D shape processing, we develop neural network approaches to tackle shape decomposition. First, we present CSGNet, a network architecture to parse shapes into CSG programs, which is trained using combination of supervised and reinforcement learning. Second, we present ParSeNet, a network architecture to decompose a shape into parametric surface patches (B-Spline) and geometric primitives (plane, cone, cylinder and sphere), trained on a large set of CAD models using supervised learning. The training of deep neural network architectures for 3D recognition and generation tasks requires a large amount of labeled datasets. We explore ways to alleviate this problem by relying on shape decomposition methods to guide the learning process. Towards that end, we first study the use of freely available metadata, albeit inconsistent, from shape repositories to learn 3D shape features. Later we show that learning to decompose a 3D shape into geometric primitives also helps in learning shape representations useful for semantic segmentation tasks. Finally, since most 3D shapes encountered in real life are textured, consisting of several fine-grained semantic parts, we propose a method to learn fine-grained representations for textured 3D shapes in a self-supervised manner by incorporating 3D geometric priors

Digital Twin Technology for Bridge Maintenance using 3D Laser Scanning: A Review

There has been a significant surge in the interest in adopting cutting-edge new technologies in the civil engineering industry in recent times that monitor the Internet of Things (IoT) data and control automation systems. By combining the real and digital worlds, digital technologies, such as Digital Twin, provide a high-level depiction of bridges and their assets. The inspection, evaluation, and management of infrastructure have experienced profound changes in technological advancement over the last decade. Technologies like laser scanners have emerged as a viable replacement for labor-intensive, costly, and dangerous traditional methods that risk health and safety. The new maintenance techniques have increased their use in the construction section, particularly regarding bridges. This review paper aims to present a comprehensive and state-of-the-art review upon using laser scanners in bridge maintenance and engineering and looking deeper into the study field in focus and researchers’ suggestions in this field. Moreover, the review was conducted to gather, evaluate, and analyze the papers collected in the years from 2017 to 2022. The interaction of research networks, dominant subfields, the co-occurrence of keywords, and countries were all examined. Four main categories were presented, namely machine learning, bridge management system (BMS), bridge information modeling (BrIM), and 3D modeling. The findings demonstrate that information standardization is the first significant obstacle to be addressed before the construction sector can benefit from the usage of Digital Twin. As a result, this article proposes a conceptual framework for building management using Digital Twins as a starting point for future research.publishedVersio

An evolutionary approach to the extraction of object construction trees from 3D point clouds

In order to extract a construction tree from a finite set of points sampled on the surface of an object, we present an evolutionary algorithm that evolves set-theoretic expressions made of primitives fitted to the input point-set and modeling operations. To keep relatively simple trees, we use a penalty term in the objective function optimized by the evolutionary algorithm. We show with experiments successes but also limitations of this approach

Reconstruction of industrial piping installations from laser point clouds using profiling techniques

Includes abstract.Includes bibliographical references (leaves 143-152).As-built models of industrial piping installations are essential for planning applications in industry. Laser scanning has emerged as the preferred data acquisition method of as built information for creating these three dimensional (3D) models. The product of the scanning process is a cloud of points representing scanned surfaces. From this point cloud, 3D models of the surfaces are reconstructed. Most surfaces are of piping elements e.g. straight pipes, t-junctions, elbows, spheres. The automatic detection of these piping elements in point clouds has the greatest impact on the reconstructed model. Various algorithms have been proposed for detecting piping elements in point clouds. However, most algorithms detect cylinders (straight pipes) and planes which make up a small percentage of piping elements found in industrial installations. In addition, these algorithms do not allow for deformation detection in pipes. Therefore, the work in this research is aimed at the detection of piping elements (straight pipes, elbows, t-junctions and flange) in point clouds including deformation detection

A parametric-assisted method for 3D generation of as-built BIM models for the built heritage

The paper outlines a parametric-assisted method for the 3D reconstruction and creation of BIM models for the built heritage. The research implements the emerging paradigms of open sourcing, cloud computing and interoperability, employing low-cost technologies (digital photogrammetry) and open source software (Grasshopper for Rhinoceros) which can ease the accessibility to a potential reuse of heritage, typically requiring high specialists and expensive equipment. The research examines the abandoned Albergo Diurno “Venezia” in Milan, heritage with a unique architectural value – a blend of Liberty and Art Deco styles. The process of 3D reconstruction of the ceiling is described. Custom algorithms have been developed to automatically rebuild the complex and irregular geometry from mesh, towards the creation of a NURBS-based 3D model. It is shown how the proposed methodology can streamline the process of data elaboration by reducing arbitrary operations and improve accuracy to preserve geometric irregularities. The associative model allows the automatic improvement in the model definition when more precise input data is feeding the algorithm, offering the opportunity to relate the precision of BIM models in accordance with the needed level of detail (LOD)

Feature preserving decimation of urban meshes

1 online resource (vii, 72 pages) : illustrations (chiefly colour), charts (chiefly colour)Includes abstract.Includes bibliographical references (pages 65-72).Commercial buildings as well as residential houses represent core structures of any modern day urban or semi-urban areas. Consequently, 3D models of urban buildings are of paramount importance to a majority of digital urban applications such as city planning, 3D mapping and navigation, video games and movies, among others. However, current studies suggest that existing 3D modeling approaches often involve high computational cost and large storage volumes for processing the geometric details of the buildings. Therefore, it is essential to generate concise digital representations of urban buildings from the 3D measurements or images, so that the acquired information can be efficiently utilized for various urban applications. Such concise representations, often referred to as “lightweight” models, strive to capture the details of the physical objects with less computational storage. Furthermore, lightweight models consume less bandwidth for online applications and facilitate accelerated visualizations. In this thesis, we provide an assessment study on state-of-the-art data structures for storing lightweight urban buildings. Then we propose a method to generate lightweight yet highly detailed 3D building models from LiDAR scans. The lightweight modeling pipeline comprises the following stages: mesh reconstruction, feature points detection and mesh decimation through gradient structure tensors. The gradient of each vertex of the reconstructed mesh is obtained by estimating the vertex confidence through eigen analysis and further encoded into a 3 X 3 structure tensor. We analyze the eigenvalues of structure tensor representing gradient variations and use it to classify vertices into various feature classes, e.g., edges, and corners. While decimating the mesh, fea ture points are preserved through a mean cost-based edge collapse operation. The experiments on different building facade models show that our method is effective in generating simplified models with a trade-off between simplification and accuracy