  Three dimensional (3D) object recognition is becoming a key desired
capability for many computer vision systems such as autonomous vehicles,
service robots and surveillance drones to operate more effectively in
unstructured environments. These real-time systems require effective
classification methods that are robust to various sampling resolutions, noisy
measurements, and unconstrained pose configurations. Previous research has
shown that points' sparsity, rotation and positional inherent variance can lead
to a significant drop in the performance of point cloud based classification
techniques. However, neither of them is sufficiently robust to multifactorial
variance and significant sparsity. In this regard, we propose a novel approach
for 3D classification that can simultaneously achieve invariance towards
rotation, positional shift, scaling, and is robust to point sparsity. To this
end, we introduce a new feature that utilizes graph structure of point clouds,
which can be learned end-to-end with our proposed neural network to acquire a
robust latent representation of the 3D object. We show that such latent
representations can significantly improve the performance of object
classification and retrieval tasks when points are sparse. Further, we show
that our approach outperforms PointNet and 3DmFV by 35.0% and 28.1%
respectively in ModelNet 40 classification tasks using sparse point clouds of
only 16 points under arbitrary SO(3) rotation.
