  Noise injection-based regularization, such as Dropout, has been widely used
in image domain to improve the performance of deep neural networks (DNNs).
However, efficient regularization in the point cloud domain is rarely
exploited, and most of the state-of-the-art works focus on data
augmentation-based regularization. In this paper, we, for the first time,
perform systematic investigation on noise injection-based regularization for
point cloud-domain DNNs. To be specific, we propose a series of regularization
techniques, namely DropFeat, DropPoint and DropCluster, to perform noise
injection on the point feature maps at the feature level, point level and
cluster level, respectively. We also empirically analyze the impacts of
different factors, including dropping rate, cluster size and dropping position,
to obtain useful insights and general deployment guidelines, which can
facilitate the adoption of our approaches across different datasets and DNN
architectures.
  We evaluate our proposed approaches on various DNN models for different point
cloud processing tasks. Experimental results show our approaches enable
significant performance improvement. Notably, our DropCluster brings 1.5%, 1.3%
and 0.8% higher overall accuracy for PointNet, PointNet++ and DGCNN,
respectively, on ModelNet40 shape classification dataset. On ShapeNet part
segmentation dataset, DropCluster brings 0.5%, 0.5% and 0.2% mean
Intersection-over-union (IoU) increase for PointNet, PointNet++ and DGCNN,
respectively. On S3DIS semantic segmentation dataset, DropCluster improves the
mean IoU of PointNet, PointNet++ and DGCNN by 3.2%, 2.9% and 3.7%,
respectively. Meanwhile, DropCluster also enables the overall accuracy increase
for these three popular backbone DNNs by 2.4%, 2.2% and 1.8%, respectively.
