Reverse engineering for industrial-environment cad models

International audienceIndustrial-environment CAD models are commonly represented by triangular meshes, which do not preserve original information about implicit surfaces used during design. The reverse-engineering algorithms presented in this paper focus on reconstructing implicit information, recovering original data. We propose two different approaches, a numerical one and an original topological approach. We explore specificities found in CAD meshes to achieve high effectiveness, reconstructing 90% of information from massive models (with millions of triangles) after few minutes of processing

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

Methodology for automatic recovering of 3D partitions from unstitched faces of non-manifold CAD models

Data exchanges between different software are currently used in industry to speed up the preparation of digital prototypes for Finite Element Analysis (FEA). Unfortunately, due to data loss, the yield of the transfer of manifold models rarely reaches 1. In the case of non-manifold models, the transfer results are even less satisfactory. This is particularly true for partitioned 3D models: during the data transfer based on the well-known exchange formats, all 3D partitions are generally lost. Partitions are mainly used for preparing mesh models required for advanced FEA: mapped meshing, material separation, definition of specific boundary conditions, etc. This paper sets up a methodology to automatically recover 3D partitions from exported non-manifold CAD models in order to increase the yield of the data exchange. Our fully automatic approach is based on three steps. First, starting from a set of potentially disconnected faces, the CAD model is stitched. Then, the shells used to create the 3D partitions are recovered using an iterative propagation strategy which starts from the so-called manifold vertices. Finally, using the identified closed shells, the 3D partitions can be reconstructed. The proposed methodology has been validated on academic as well as industrial examples.This work has been carried out under a research contract between the Research and Development Direction of the EDF Group and the Arts et Métiers ParisTech Aix-en-Provence

Coarse to fine : toward an intelligent 3D acquisition system

International audienceThe 3D acquisition-compression-processing chain is , most of the time , sequenced into independent stages. As resulting , a large amount of 3D points are acquired whatever the geometry of the object and the processing to be done in further steps. It appears , particularly in mechanical part 3D modeling and in CAD , that the acquisition of such an amount of data is not always mandatory. We propose a method aiming at minimizing the number of 3D points to be acquired with respect to the local geometry of the part and therefore to compress the cloud of points during the acquisition stage. The method we propose is based on a new coarse to fine approach in which from a coarse set of 2D points associated to the local normals the 3D object model is segmented into a combination of primitives. The obtained model is enriched where it is needed with new points and a new primitive extraction stage is performed in the refined regions. This is done until a given precision of the reconstructed object is attained. It is noticeable that contrary to other studies we do not work on a meshed model but directly on the data provided by the scanning device

A coarse to fine 3D acquisition system

International audienceThe 3D chain (acquisition-processing-compression) is , most of the time , sequenced into several steps. Such approaches result into an one-dense acquisition of 3D points. In large scope of applications , the first processing step consists in simplifying the data. In this paper , we propose a coarse to fine acquisition system which permits to obtain simplified data directly from the acquisition. By calculating some complementary information from 2D images , such as 3D normals , multiple homogeneous regions will be segmented and affected to a given primitive class. Contrary to other studies , the whole process is not based on a mesh. The obtained model is simplified directly from the 2D data acquired by a 3D scanner

An interactive mesh generation environment for geometry-based simulations. Progress updated

We presented an overview of an interactive mesh generation environment in our previous work that it is being developed in the Laboratori de Càlcul Numèric (LaCàN). We started the development of the software in order to unify available legacy code, new developments and research algorithms in only one mesh generation package. This paper presents an update to the last environment overview after significant implementation and conceptual development. We provide a brief summary of: New features, as submapping and data output suitable for geometry-based methods as; current developments, as GUI improvement and 3D modelling tools; and future features, such as the command interpreter. Finally, we conclude that chosen mesh generation paradigm and software engineering concepts have allowed us to improve and scale the environment since last revision

A Minimalist Approach to Type-Agnostic Detection of Quadrics in Point Clouds

This paper proposes a segmentation-free, automatic and efficient procedure to detect general geometric quadric forms in point clouds, where clutter and occlusions are inevitable. Our everyday world is dominated by man-made objects which are designed using 3D primitives (such as planes, cones, spheres, cylinders, etc.). These objects are also omnipresent in industrial environments. This gives rise to the possibility of abstracting 3D scenes through primitives, thereby positions these geometric forms as an integral part of perception and high level 3D scene understanding. As opposed to state-of-the-art, where a tailored algorithm treats each primitive type separately, we propose to encapsulate all types in a single robust detection procedure. At the center of our approach lies a closed form 3D quadric fit, operating in both primal & dual spaces and requiring as low as 4 oriented-points. Around this fit, we design a novel, local null-space voting strategy to reduce the 4-point case to 3. Voting is coupled with the famous RANSAC and makes our algorithm orders of magnitude faster than its conventional counterparts. This is the first method capable of performing a generic cross-type multi-object primitive detection in difficult scenes. Results on synthetic and real datasets support the validity of our method.Comment: Accepted for publication at CVPR 201