The descriptor is then obtained by concatenating the computed histograms.Ĭolor information can also be incorporated to the descriptor in order to increase its discriminative power. Optionally, interpolation may be used to distribute the value of each sample into adjacent cells, in an attempt to avoid boundary effects that may cause abrupt changes to the histogram when a sample shifts from being within one cell to another.
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The contribution of each sample to the histogram is normalized with respect to the total number of points in the cloud. If, then for each cell it will be computed a histogram of normalized distances between each sample and the cloud centroid. If, then each histogram bin will store the number of points that belong to its correspondent cell in the 3D regular grid.
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For each grid cell, a histogram with bins is computed. The point cloud axis-aligned bounding cube centered on the origin is divided into an regular grid. Once the point cloud is aligned using the reference frame, a pose invariant global shape descriptor can be computed from it. Then a covariance matrix is computed from and as follows: Given a set of 3D points that represents a partial view of an object, with, the first step consists in computing their centroid, which is the origin of the reference frame. The reference frame is estimated using a Principal Component Analysis (PCA) approach. The pose of each recognized object is also computed from the alignment transforms of matched query and train partial views. Object recognition is then performed by matching query and train descriptors of partial views.
![buildsoft global estimating tutorial buildsoft global estimating tutorial](https://image.slidesharecdn.com/software-assignment-171124153242/95/software-assignment-2-638.jpg)
Color distribution along the point cloud can also be taken into account for obtaining a shape and color descriptor with a higher discriminative power. After alignment, a shape descriptor is computed for the point cloud based on the spatial distribution of the 3D points. The first step consists in estimating a reference frame for the point cloud, which allows the computation of a transform that aligns it to the canonical coordinate system, making the descriptor pose invariant. The Globally Aligned Spatial Distribution (or GASD) global description method takes as input a 3D point cloud that represents a partial view of a given object.