  Estimation of surface curvature from range data is important for a range of
tasks in computer vision and robotics, object segmentation, object recognition
and robotic grasping estimation. This work presents a fast method of robustly
computing accurate metric principal curvature values from noisy point clouds
which was implemented on GPU. In contrast to existing readily available
solutions which first differentiate the surface to estimate surface normals and
then differentiate these to obtain curvature, amplifying noise, our method
iteratively fits parabolic quadric surface patches to the data. Additionally
previous methods with a similar formulation use less robust techniques less
applicable to a high noise sensor. We demonstrate that our method is fast and
provides better curvature estimates than existing techniques. In particular we
compare our method to several alternatives to demonstrate the improvement.
