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

Photo-based 3D scanning vs. laser scanning : competitive data acquisition methods for digital terrain modelling of steep mountain slopes

The paper presents how terrestrial laser scanning (TLS) and terrestrial digital photogrammetry were used to create a 3D model of a steep mountain wall. Terrestrial methods of data acquisition are the most suitable for such relief, as the most effective registration is perpendicular to the surface. First, various aspects of photo-based scanning and laser scanning were discussed. The general overview of both technologies was followed by the description of a case study of the western wall of the Kościelec Mountain (2155 m). The case study area is one of the most interesting and popular rock climbing areas in the Polish High Tatra Mts. The wall is about 300 meters high, has varied relief and some parts are overhung. Triangular irregular mesh was chosen to represent the true3D surface with its complicated relief. To achieve a more smooth result for visualization NURBS curves and surfaces were utilized. Both 3D models were then compared to the standard DTM of the Tatra Mountains in TIN format, obtained from aerial photographs (0.2 m ground pixel size). The results showed that both TLS and terrestrial photogrammetry had similar accuracy and level of detail and could effectively supplement very high resolution DTMs of the mountain areas

Adaptive Methods for Point Cloud and Mesh Processing

Point clouds and 3D meshes are widely used in numerous applications ranging from games to virtual reality to autonomous vehicles. This dissertation proposes several approaches for noise removal and calibration of noisy point cloud data and 3D mesh sharpening methods. Order statistic filters have been proven to be very successful in image processing and other domains as well. Different variations of order statistics filters originally proposed for image processing are extended to point cloud filtering in this dissertation. A brand-new adaptive vector median is proposed in this dissertation for removing noise and outliers from noisy point cloud data. The major contributions of this research lie in four aspects: 1) Four order statistic algorithms are extended, and one adaptive filtering method is proposed for the noisy point cloud with improved results such as preserving significant features. These methods are applied to standard models as well as synthetic models, and real scenes, 2) A hardware acceleration of the proposed method using Microsoft parallel pattern library for filtering point clouds is implemented using multicore processors, 3) A new method for aerial LIDAR data filtering is proposed. The objective is to develop a method to enable automatic extraction of ground points from aerial LIDAR data with minimal human intervention, and 4) A novel method for mesh color sharpening using the discrete Laplace-Beltrami operator is proposed. Median and order statistics-based filters are widely used in signal processing and image processing because they can easily remove outlier noise and preserve important features. This dissertation demonstrates a wide range of results with median filter, vector median filter, fuzzy vector median filter, adaptive mean, adaptive median, and adaptive vector median filter on point cloud data. The experiments show that large-scale noise is removed while preserving important features of the point cloud with reasonable computation time. Quantitative criteria (e.g., complexity, Hausdorff distance, and the root mean squared error (RMSE)), as well as qualitative criteria (e.g., the perceived visual quality of the processed point cloud), are employed to assess the performance of the filters in various cases corrupted by different noisy models. The adaptive vector median is further optimized for denoising or ground filtering aerial LIDAR data point cloud. The adaptive vector median is also accelerated on multi-core CPUs using Microsoft Parallel Patterns Library. In addition, this dissertation presents a new method for mesh color sharpening using the discrete Laplace-Beltrami operator, which is an approximation of second order derivatives on irregular 3D meshes. The one-ring neighborhood is utilized to compute the Laplace-Beltrami operator. The color for each vertex is updated by adding the Laplace-Beltrami operator of the vertex color weighted by a factor to its original value. Different discretizations of the Laplace-Beltrami operator have been proposed for geometrical processing of 3D meshes. This work utilizes several discretizations of the Laplace-Beltrami operator for sharpening 3D mesh colors and compares their performance. Experimental results demonstrated the effectiveness of the proposed algorithms

Sistema de reconstrucción 3D del cuerpo humano a partir de múltiples vistas RGB-D

La generación de modelos virtuales en tres dimensiones que imiten la forma y apariencia de objetos reales es un proceso que cada día cuenta con más utilidades en una amplia variedad de sectores. Para llevar a cabo esta tarea contamos con tantas posibilidades casi como utilidades y además estas crecen conforme vamos creando hardware más potente. La investigación en este sector siempre ha sido un objetivo difícil y es que la reconstrucción de objetos tridimensionales a partir de cuerpos reales a menudo ha estado considerada como un complejo problema científico. El principal objetivo de este proyecto es la generación de un modelo de representación visual 3D del cuerpo humano a partir de diversas capturas desde un sistema multicámara rgb-d. Como punto de partida se deberá llevar a cabo un calibrado para posteriormente obtener nubes de puntos de cada una de las vistas. Una vez obtenidas se filtrarán y se alinearán con la idea de obtener un registro, al que posteriormente se le aplicará un mallado. Para finalizar se texturizará el modelo y exportaremos el resultado en un formato que pueda observarse mediante realidad virtual

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