  The Discriminative Optimization (DO) algorithm has been proved much
successful in 3D point cloud registration. In the original DO, the feature
(descriptor) of two point cloud was defined as a histogram, and the element of
histogram indicates the weights of scene points in "front" or "back" side of a
model point. In this paper, we extended the histogram which indicate the sides
from "front-back" to "front-back", "up-down", and "clockwise-anticlockwise". In
addition, we reweighted the extended histogram according to the model points'
distribution. We evaluated the proposed Improved DO on the Stanford Bunny and
Oxford SensatUrban dataset, and compared it with six classical State-Of-The-Art
point cloud registration algorithms. The experimental result demonstrates our
algorithm achieves comparable performance in point registration accuracy and
root-mean-sqart-error.
