  3D point cloud interpretation is a challenging task due to the randomness and
sparsity of the component points. Many of the recently proposed methods like
PointNet and PointCNN have been focusing on learning shape descriptions from
point coordinates as point-wise input features, which usually involves
complicated network architectures. In this work, we draw attention back to the
standard 3D convolutions towards an efficient 3D point cloud interpretation.
Instead of converting the entire point cloud into voxel representations like
the other volumetric methods, we voxelize the sub-portions of the point cloud
only at necessary locations within each convolution layer on-the-fly, using our
dynamic voxelization operation with self-adaptive voxelization resolution. In
addition, we incorporate 3D group convolution into our dense convolution kernel
implementation to further exploit the rotation invariant features of point
cloud. Benefiting from its simple fully-convolutional architecture, our network
is able to run and converge at a considerably fast speed, while yields on-par
or even better performance compared with the state-of-the-art methods on
several benchmark datasets.
