For this kind of processing a quite faithful geometric representation is not needed, but it is strongly needed that the overall topology is the right one. A reconstruction at depth 9 is usually good, that generates a mesh of 1.3M of faces. Poisson surface reconstruction is a perfect filter for this task. So the first step is to build a watertight, coarser but topologically sound model. The mesh of the skull is composed by 1.000.000 triangles, it has a meaningful per-vertex color (recovered from a set of photos) and, as it often happens, it is topologically dirty.įirst of all it is non 2-manifold (there are 7 edges where more than two face are incident) than there are many small holes and handles that make difficult any kind of parametrization. You can see it depicted in the two small figures on the right. Let's start from a medium complexity mesh of a skull (kindly provided and scanned for the VCG Lab by Marco Callieri). Now some a two-part tutorial on his practical usage. In the last release of MeshLab we included our state-of-the-art parametrization/remeshing algorithm based on abstract parametrization. MeshLab is used in various academic and research contexts, like microbiology, cultural heritage, surface reconstruction, paleontology, for rapid prototyping in orthopedic surgery, in orthodontics, and desktop manufacturing.In the pipeline of processing 3D data, after you have aligned and merged your range maps, you ofter require to get a nice clean textured mesh. MeshLab is used in various academic and research contexts, like microbiology, cultural heritage, surface reconstruction, paleontology, for rapid prototyping in orthopedic surgery, in orthodontics, and desktop manufacturing. MeshLab can also import point clouds reconstructed using Photosynth. The system supports input/output in the following formats: PLY, STL, OFF, OBJ, 3DS, VRML 2.0, U3D, X3D and COLLADA. MeshLab is available for most platforms, including Windows, Linux, Mac OS X, and, with reduced functionality, on iOS and Android and even as a pure client-side JavaScript application called MeshLabJS. MeshLab also includes an interactive direct paint-on-mesh system that allows to interactively change the color of a mesh, to define selections and to directly smooth out noise and small features. It includes a tool for the registration of multiple range maps based on the iterative closest point algorithm. For the removal of noise, usually present in acquired surfaces, MeshLab supports various kinds of smoothing filters and tools for curvature analysis and visualisation. Remeshing tools support high quality simplification based on quadric error measure, various kinds of subdivision surfaces, and two surface reconstruction algorithms from point clouds based on the ball-pivoting technique and on the Poisson surface reconstruction approach. The automatic mesh cleaning filters includes removal of duplicated, unreferenced vertices, non-manifold edges, vertices, and null faces. It is a general-purpose system aimed at the processing of the typical not-so-small unstructured 3D models that arise in the 3D scanning pipeline. MeshLab is developed by the ISTI - CNR research center initially MeshLab was created as a course assignment at the University of Pisa in late 2005. It is well known in the more technical fields of 3D development and data handling. MeshLab is free and open-source software, subject to the requirements of the GNU General Public License (GPL), version 2 or later, and is used as both a complete package and a library powering other software. MeshLab is an advanced 3D mesh processing software system that is oriented to the management and processing of unstructured large meshes and provides a set of tools for editing, cleaning, healing, inspecting, rendering, and converting these kinds of meshes.
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