  3D shape models that directly classify objects from 3D information have
become more widely implementable. Current state of the art models rely on deep
convolutional and inception models that are resource intensive. Residual neural
networks have been demonstrated to be easier to optimize and do not suffer from
vanishing/exploding gradients observed in deep networks. Here we implement a
residual neural network for 3D object classification of the 3D Princeton
ModelNet dataset. Further, we show that widening network layers dramatically
improves accuracy in shallow residual nets, and residual neural networks
perform comparable to state-of-the-art 3D shape net models, and we show that
widening network layers improves classification accuracy. We provide extensive
training and architecture parameters providing a better understanding of
available network architectures for use in 3D object classification.
