  We propose a neural network for 3D point cloud processing that exploits
`spherical' convolution kernels and octree partitioning of space. The proposed
metric-based spherical kernels systematically quantize point neighborhoods to
identify local geometric structures in data, while maintaining the properties
of translation-invariance and asymmetry. The network architecture itself is
guided by octree data structuring that takes full advantage of the sparse
nature of irregular point clouds. We specify spherical kernels with the help of
neurons in each layer that in turn are associated with spatial locations. We
exploit this association to avert dynamic kernel generation during network
training, that enables efficient learning with high resolution point clouds. We
demonstrate the utility of the spherical convolutional neural network for 3D
object classification on standard benchmark datasets.
