  Access to 3D point cloud representations has been widely facilitated by LiDAR
sensors embedded in various mobile devices. This has led to an emerging need
for fast and accurate point cloud processing techniques. In this paper, we
revisit and dive deeper into PointNet++, one of the most influential yet
under-explored networks, and develop faster and more accurate variants of the
model. We first present a novel Separable Set Abstraction (SA) module that
disentangles the vanilla SA module used in PointNet++ into two separate
learning stages: (1) learning channel correlation and (2) learning spatial
correlation. The Separable SA module is significantly faster than the vanilla
version, yet it achieves comparable performance. We then introduce a new
Anisotropic Reduction function into our Separable SA module and propose an
Anisotropic Separable SA (ASSA) module that substantially increases the
network's accuracy. We later replace the vanilla SA modules in PointNet++ with
the proposed ASSA module, and denote the modified network as ASSANet. Extensive
experiments on point cloud classification, semantic segmentation, and part
segmentation show that ASSANet outperforms PointNet++ and other methods,
achieving much higher accuracy and faster speeds. In particular, ASSANet
outperforms PointNet++ by $7.4$ mIoU on S3DIS Area 5, while maintaining $1.6
\times $ faster inference speed on a single NVIDIA 2080Ti GPU. Our scaled
ASSANet variant achieves $66.8$ mIoU and outperforms KPConv, while being more
than $54 \times$ faster.
