12/28/2022 0 Comments K 3d measureThey calculated the plane Voronoi diagram of the projected points so as to insert new points equidistant from the first. proposed to enrich the sample in the undersampled regions by considering the projection of these on a plan. This method does not guarantee the density of the resulting sample points. The points for which this distance is the smallest are removed. The distance between the position of the point and its projection on the surface provides a measure of error. They calculate the contribution of a point to this surface by projecting it onto an MLS surface estimated from neighboring points. estimated the local geometrical properties of the sampled surface using a Moving Least Squares (MLS) model of the underlying surface, which requires having oriented normal in a consistent manner. This approximation is calculated from the Delaunay triangulation of the sample of input points, which has the drawback of very large samples. used an approximation of the LFS (local feature size) of the sampled area. The algorithm has the disadvantage of not giving any guarantee on the density of the resulting set of points. The points with the weakest measurement are removed iteratively. Linsen presented a technique that associates a scalar value with each point locally measuring the average variation of certain information, such as the proximity of neighbors or the direction of normal. However, this method is penalized by the cost of its initialization and that of the relaxation phase for large point samples. This method makes it possible to generate high quality splat covers for smooth surfaces, by filtering noise. A relaxation phase can be applied to determine an optimal position for the remaining splats. During this process, the regularity of the distribution is not checked. For each splat processed, the points it covers are projected onto its plane, and then only the splats associated with the points projected inside the convex envelope of the projected points are eliminated. To guarantee the recovery of the entire sampled surface, the algorithm proceeds as follows. In the second step, the redundant splats are eliminated during a filtering process of the surface expansion type. The first step of the method consists in locally approximating the surface at each point of the sample by a circular or elliptical plane surface element called a splat. Wu and Kobbelt computed an optimal set of splats to cover a sampled surface. Due to the spatial nature of this approach, it is difficult to control the quality of the distribution of points on the sampled surface. The partitioning criterion depends both on a maximum number of points and on variations in local geometry in a region. The cutting planes are defined by the centre and the main direction of each region. proposed a method based on hierarchical decomposition of the sample of points, calculated by binary partition of space. Several scientific articles have studied and presented simplification methods. It should be noted that samples a surface close to the original surface that is sampled by. After simplification, we obtain a simplified point cloud such that. Simplification of a 3D set of points can be defined as follows: being given an original surface presented by a point cloud such that, simplification of consists of calculating a point cloud such that, knowing that is a cardinality. This device may be broken down into three primary sorts: contact, active noncontact, and passive noncontact. The scanning of a real object is facilitated by a device called 3D scanner. This step ensures the optimization of the number of points that constitute the 3D point cloud. The simplification of a 3D point cloud, obtained from the digitization of a real object, is a primordial and important step in the field of 3D reconstruction. Numerous experiments demonstrate the effectiveness of the proposed simplification method of 3D point cloud. In this paper, MATLAB is used to carry out the simulation, and the performance of our method is testified by test dataset. Then, an entropy estimation is performed for each cluster to remove the ones that have minimal entropy. Initially, 3D point cloud is divided into clusters using k-means algorithm. In this paper, a new approach is proposed to simplify 3D point cloud based on k-nearest neighbor ( k-NN) and clustering algorithm. This is due to the huge amounts of dense 3D point cloud produced by 3D scanning devices. While the reconstruction of 3D objects is increasingly used today, the simplification of 3D point cloud, however, becomes a substantial phase in this process of reconstruction.
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