#MESHLAB FILL HOLES SOFTWARE#

In this paper, we propose a method to fill in these undesirable holes while retrieving the complex shapes the scanner was unable to capture. This is not acceptable for the forthcoming steps of the redesign process. It results in deficiencies in the points cloud and, in a set of holes in the resulting mesh. This can be due to the object complexity and/or to the type of acquisition system. The main drawback lies in the fact that some areas of the object outer surface may never be accessible. The reconstructed geometric model is usually a mesh built from a points cloud acquired with a scanner. The Reverse Engineering process consists of creating a digital representation of a physical model. Our results validate the accuracy of ScReAM for reconstructing a mesh for volume calculation. Evaluations are derived from a sample size of 47 subjects that were scanned by two different 3D optical scanners.

#MESHLAB FILL HOLES SOFTWARE#

We compare ScReAM with Alias and MeshFix which are well-known software used for triangular meshing. In this study, we present ScReAM (Scan Reconstruction for Anthropometric Measurements) which is a fully automated geometrical 3D reconstruction approach to find and fill these holes. These need to be repaired in order to obtain correct landmark detection and automatic calculation of anthropometric measurements-especially body volume. Three-dimensional (3D) optical devices can rapidly acquire body surface scans in the form of a triangular mesh which can then be used to obtain anthropometric measurements such as body volume, limb lengths, and circumferences however, the meshes provided by some scanners may include missing data patches known as holes. The proposed hole-detection algorithm found almost all of the holes in different scenes and qualitatively, the subsequent repairs are difficult to see with the naked eye.ДЄnthropometric measurements have been used to assess an individual's body composition, disease risk, and nutritional status. Experimental results demonstrate the visual effect of the proposed method and the quality of the generated meshes, relative to the existing methods.

The novel method against the existing methods have been tested on different types of holes in four scenes.

The proposed method, detects the boundary holes robustly by constructing local overlap judgments, and provides the possibility for sharp features recovery using local structure information, as well as reduces the cost of maintaining manifold meshes thus enhancing their utility. To solve these problems, LIMOFilling, a new method for filling holes in 3D manifold meshes was proposed, and recovering the sharp features. The existing hole-filling methods do not have the ability to detect holes at the model boundaries, leaving overlaps between the newly generated triangles, and also lack the ability to recover missing sharp features in the hole-region. The complexity of these objects and self-occlusion, however, can cause cameras to miss some areas, creating holes in the model. Manifold mesh, a triangular network for representing 3D objects, is widely used to reconstruct accurate 3D models of objects structure.

Meshlab fill holes