Sentence examples for between two point clouds from inspiring English sources
Figure 7 shows all the steps that must be followed in order to obtain a coarse alignment between two point clouds.
The iterative closest point (ICP) algorithm [5] is the de facto standard to compute the rigid transformation T between two point clouds.
The best performing variants employed strategies that filtered the data sets, used local surface geometry in the form normals, and used the distance between points in one point cloud to a corresponding surface from a reference point cloud as a measure of the fit between two point clouds.
Nevertheless, searching the optimal alignment between two point clouds the accuracy of marker-less surface tracking suffers from the lack of point-to-point correspondence [ 10] and is further decreased by elastic deformations of the human skin.
The Iterative Closest Point (ICP) algorithm seeks to minimize the misalignment between two point cloud data sets.
For stable areas (such as nonglacial surfaces), vertical differences were detected between the two point clouds (RMS M3C2 vertical difference of 8.5 m), with the outwash zones adjacent to the assessed glacier termini showing less extensive vertical discrepancies (94% of M3C2 vertical differences between ± 5 m).
For each simulation, we choose two point clouds that correspond to two different time frames.
Finally, two point clouds representing the lumen and vessel wall were derived for each artery and were processed to nonuniform rational B-spline (NURBS) 3D surfaces.
Figure 4 shows an oblique view of the point clouds, and Table 1 summarizes the properties of the four point clouds for each TLS measurement.
The algorithm performs an iterative comparison between 2D descriptors by using a purpose-designed similarity measure in order to find correspondences between two 3D point clouds sensed from different positions of a free-form object.
