Feature Preserving Mesh Generation from 3D Point Clouds

Special issue for EUROGRAPHICS Symposium on Geometry Processing, Lyon 2010International audienceWe address the problem of generating quality surface triangle meshes from 3D point clouds sampled on piecewise smooth surfaces. Using a feature detection process based on the covariance matrices of Voronoi cells, we first ex- tract from the point cloud a set of sharp features. Our algorithm also runs on the input point cloud a reconstruction process, such as Poisson reconstruction, providing an implicit surface. A feature preserving variant of a Delaunay refinement process is then used to generate a mesh approximating the implicit surface and containing a faithful representation of the extracted sharp edges. Such a mesh provides an enhanced trade-off between accuracy and mesh complexity. The whole process is robust to noise and made versatile through a small set of parameters which govern the mesh sizing, approximation error and shape of the elements. We demonstrate the effectiveness of our method on a variety of models including laser scanned datasets ranging from indoor to outdoor scenes

Voronoi-Based Curvature and Feature Estimation from Point Clouds

International audienceWe present an efficient and robust method for extracting curvature information, sharp features, and normal directions of a piecewise smooth surface from its point cloud sampling in a unified framework. Our method is integral in nature and uses convolved covariance matrices of Voronoi cells of the point cloud which makes it provably robust in the presence of noise. We show that these matrices contain information related to curvature in the smooth parts of the surface, and information about the directions and angles of sharp edges around the features of a piecewise-smooth surface. Our method is applicable in both two and three dimensions, and can be easily parallelized, making it possible to process arbitrarily large point clouds, which was a challenge for Voronoi-based methods. In addition, we describe a Monte-Carlo version of our method, which is applicable in any dimension. We illustrate the correctness of both principal curvature information and feature extraction in the presence of varying levels of noise and sampling density on a variety of models. As a sample application, we use our feature detection method to segment point cloud samplings of piecewise-smooth surfaces

Inference-based Geometric Modeling for the Generation of Complex Cluttered Virtual Environments

As the use of simulation increases across many diff erent application domains, the need for high- fidelity three-dimensional virtual representations of real-world environments has never been greater. This need has driven the research and development of both faster and easier methodologies for creating such representations. In this research, we present two diff erent inference-based geometric modeling techniques that support the automatic construction of complex cluttered environments. The fi rst method we present is a surface reconstruction-based approach that is capable of reconstructing solid models from a point cloud capture of a cluttered environment. Our algorithm is capable of identifying objects of interest amongst a cluttered scene, and reconstructing complete representations of these objects even in the presence of occluded surfaces. This approach incorporates a predictive modeling framework that uses a set of user provided models for prior knowledge, and applies this knowledge to the iterative identifi cation and construction process. Our approach uses a local to global construction process guided by rules for fi tting high quality surface patches obtained from these prior models. We demonstrate the application of this algorithm on several synthetic and real-world datasets containing heavy clutter and occlusion. The second method we present is a generative modeling-based approach that can construct a wide variety of diverse models based on user provided templates. This technique leverages an inference-based construction algorithm for developing solid models from these template objects. This algorithm samples and extracts surface patches from the input models, and develops a Petri net structure that is used by our algorithm for properly fitting these patches in a consistent fashion. Our approach uses this generated structure, along with a defi ned parameterization (either user-defi ned through a simple sketch-based interface or algorithmically de fined through various methods), to automatically construct objects of varying sizes and con figurations. These variations can include arbitrary articulation, and repetition and interchanging of parts sampled from the input models. Finally, we affim our motivation by showing an application of these two approaches. We demonstrate how the constructed environments can be easily used within a physically-based simulation, capable of supporting many diff erent application domains

Patch-graph Reconstruction for Piecewise Smooth Surfaces

In this paper we present a new surface reconstruction technique for piecewise smooth surfaces from point clouds, such as scans of architectural sites or man-made artifacts. The technique operates in three conceptual steps: First, a graph of local surface patches, each consisting of a set of basis functions, is assembled. Second, we establish topological connectivity among the nodes that respects sharp features. Third, we find optimal coefficients for the basis functions in each node by solving a sparse optimization problem. Our final representation allows for robust finding of crease and border edges which separate the piecewise smooth parts. As output of our approach, we extract a clean, manifold surface mesh which preserves and even aggravates feature lines. The main benefit of our new proposal in comparison to previous work is its robustness and efficiency, which we examine by applying the algorithm to a variety of different synthetic and real-word data sets