  In this paper, we present a robust spherical harmonics approach for the
classification of point cloud-based objects. Spherical harmonics have been used
for classification over the years, with several frameworks existing in the
literature. These approaches use variety of spherical harmonics based
descriptors to classify objects. We first investigated these frameworks
robustness against data augmentation, such as outliers and noise, as it has not
been studied before. Then we propose a spherical convolution neural network
framework for robust object classification. The proposed framework uses the
voxel grid of concentric spheres to learn features over the unit ball. Our
proposed model learn features that are less sensitive to data augmentation due
to the selected sampling strategy and the designed convolution operation. We
tested our proposed model against several types of data augmentation, such as
noise and outliers. Our results show that the proposed model outperforms the
state of art networks in terms of robustness to data augmentation.
