  It is possible to associate a highly constrained subset of relative 6 DoF
poses between two 3D shapes, as long as the local surface orientation, the
normal vector, is available at every surface point. Local shape features can be
used to find putative point correspondences between the models due to their
ability to handle noisy and incomplete data. However, this correspondence set
is usually contaminated by outliers in practical scenarios, which has led to
many past contributions based on robust detectors such as the Hough transform
or RANSAC. The key insight of our work is that a single correspondence between
oriented points on the two models is constrained to cast votes in a 1 DoF
rotational subgroup of the full group of poses, SE(3). Kernel density
estimation allows combining the set of votes efficiently to determine a full 6
DoF candidate pose between the models. This modal pose with the highest density
is stable under challenging conditions, such as noise, clutter, and occlusions,
and provides the output estimate of our method.
  We first analyze the robustness of our method in relation to noise and show
that it handles high outlier rates much better than RANSAC for the task of 6
DoF pose estimation. We then apply our method to four state of the art data
sets for 3D object recognition that contain occluded and cluttered scenes. Our
method achieves perfect recall on two LIDAR data sets and outperforms competing
methods on two RGB-D data sets, thus setting a new standard for general 3D
object recognition using point cloud data.
