Goal: to encode a point’s k-neighborhood geometrical properties by generalizing the mean curvature around the point using a multi-dimensional histogram of values.
A Point Feature Histogram representation is based on the relationships between the points in the k-neighborhood and their estimated surface normals. It attempts to capture as best as possible the sampled surface variations by taking into account all the interactions between the directions of the estimated normals. The resultant hyperspace is thus dependent on the quality of the surface normal estimations at each point.
