  Channel pruning can effectively reduce both computational cost and memory
footprint of the original network while keeping a comparable accuracy
performance. Though great success has been achieved in channel pruning for 2D
image-based convolutional networks (CNNs), existing works seldom extend the
channel pruning methods to 3D point-based neural networks (PNNs). Directly
implementing the 2D CNN channel pruning methods to PNNs undermine the
performance of PNNs because of the different representations of 2D images and
3D point clouds as well as the network architecture disparity. In this paper,
we proposed CP$^3$, which is a Channel Pruning Plug-in for Point-based network.
CP$^3$ is elaborately designed to leverage the characteristics of point clouds
and PNNs in order to enable 2D channel pruning methods for PNNs. Specifically,
it presents a coordinate-enhanced channel importance metric to reflect the
correlation between dimensional information and individual channel features,
and it recycles the discarded points in PNN's sampling process and reconsiders
their potentially-exclusive information to enhance the robustness of channel
pruning. Experiments on various PNN architectures show that CP$^3$ constantly
improves state-of-the-art 2D CNN pruning approaches on different point cloud
tasks. For instance, our compressed PointNeXt-S on ScanObjectNN achieves an
accuracy of 88.52% with a pruning rate of 57.8%, outperforming the baseline
pruning methods with an accuracy gain of 1.94%.
