  In this paper, we diagnose deep neural networks for 3D point cloud processing
to explore utilities of different intermediate-layer network architectures. We
propose a number of hypotheses on the effects of specific intermediate-layer
network architectures on the representation capacity of DNNs. In order to prove
the hypotheses, we design five metrics to diagnose various types of DNNs from
the following perspectives, information discarding, information concentration,
rotation robustness, adversarial robustness, and neighborhood inconsistency. We
conduct comparative studies based on such metrics to verify the hypotheses. We
further use the verified hypotheses to revise intermediate-layer architectures
of existing DNNs and improve their utilities. Experiments demonstrate the
effectiveness of our method.
