  With the development of 3D sensing technologies, point clouds have attracted
increasing attention in a variety of applications for 3D object representation,
such as autonomous driving, 3D immersive tele-presence and heritage
reconstruction. However, it is challenging to process large-scale point clouds
in terms of both computation time and storage due to the tremendous amounts of
data. Hence, we propose a point cloud simplification algorithm, aiming to
strike a balance between preserving sharp features and keeping uniform density
during resampling. In particular, leveraging on graph spectral processing, we
represent irregular point clouds naturally on graphs, and propose concise
formulations of feature preservation and density uniformity based on graph
filters. The problem of point cloud simplification is finally formulated as a
trade-off between the two factors and efficiently solved by our proposed
algorithm. Experimental results demonstrate the superiority of our method, as
well as its efficient application in point cloud registration.
