An efficient method for 3d digitized data reduction for reverse engineering

Abstract EN

This paper addresses the problem of data reduction for reverse engineering.

We present an efficient method for managing the amount of data points acquired by 3D laser scanner.

The proposed method is based on computing the surface normal which is fundamental in the most of reverse engineering algorithms.

The normal vectors are calculated by fitting the best quadratic or cubic function to the neighboring points.

However, a point is assigned to the normal.

Then, the data points are partitioned into cells based on their positions of x, y, and z-axis, respectively.

In each cell, the angle between an arbitrary direction and the normal is obtained.

Each cell is then subdivided into cells based on the angles.

Finally, the amount of points is reduced by sampling the representative points for each cell.

The performance of the proposed method is illustrated using a range of point clouds scanned from typical engineering surfaces.