  Currently, the area of geometric modeling and the construction of 3D models
based on point clouds from laser sensors is actively developing. One of the
basic tasks of geometric modeling is the reconstruction of a surface from a
cloud of points. The aim of this work is to develop a parallel least-squares
surface reconstruction method on a distributed memory supercomputer that allows
achieving optimal results both in scalability and surface reconstruction
quality. The focus of the work is on the surface reconstruction algorithm based
on the least squares method, in connection with which the algorithm was called
the moving least squares method.

Distributed, Parallel, and Cluster Computing