  In this work, we propose a novel framework shape back-projection for
computationally efficient point cloud processing in a probabilistic manner. The
primary component of the technique is shape histogram and a back-projection
procedure. The technique measures similarity between 3D surfaces, by analyzing
their geometrical properties. It is analogous to color back-projection which
measures similarity between images, simply by looking at their color
distributions. In the overall process, first, shape histogram of a sample
surface (e.g. planar) is computed, which captures the profile of surface
normals around a point in form of a probability distribution. Later, the
histogram is back-projected onto a test surface and a likelihood score is
obtained. The score depicts that how likely a point in the test surface behaves
similar to the sample surface, geometrically. Shape back-projection finds its
application in binary surface classification, high curvature edge detection in
unorganized point cloud, automated point cloud labeling for 3D-CNNs
(convolutional neural network) etc. The algorithm can also be used for
real-time robotic operations such as autonomous object picking in warehouse
automation, ground plane extraction for autonomous vehicles and can be deployed
easily on computationally limited platforms (UAVs).
