  Point-cloud is an efficient way to represent 3D world. Analysis of
point-cloud deals with understanding the underlying 3D geometric structure. But
due to the lack of smooth topology, and hence the lack of neighborhood
structure, standard correlation can not be directly applied on point-cloud. One
of the popular approaches to do point correlation is to partition the
point-cloud into voxels and extract features using standard 3D correlation. But
this approach suffers from sparsity of point-cloud and hence results in
multiple empty voxels. One possible solution to deal with this problem is to
learn a MLP to map a point or its local neighborhood to a high dimensional
feature space. All these methods suffer from a large number of parameters
requirement and are susceptible to random rotations. A popular way to make the
model "invariant" to rotations is to use data augmentation techniques with
small rotations but the potential drawback includes \item more training samples
\item susceptible to large rotations. In this work, we develop a rotation
invariant point-cloud segmentation and classification scheme based on the
omni-directional camera model (dubbed as {\bf POIRot$^1$}). Our proposed model
is rotationally invariant and can preserve geometric shape of a 3D point-cloud.
Because of the inherent rotation invariant property, our proposed framework
requires fewer number of parameters (please see \cite{Iandola2017SqueezeNetAA}
and the references therein for motivation of lean models). Several experiments
have been performed to show that our proposed method can beat the
state-of-the-art algorithms in classification and part segmentation
applications.
