  We present a novel lightweight convolutional neural network for point cloud
analysis. In contrast to many current CNNs which increase receptive field by
downsampling point cloud, our method directly operates on the entire point sets
without sampling and achieves good performances efficiently. Our network
consists of point voxel convolution (PVC) layer as building block. Each layer
has two parallel branches, namely the voxel branch and the point branch. For
the voxel branch specifically, we aggregate local features on non-empty voxel
centers to reduce geometric information loss caused by voxelization, then apply
volumetric convolutions to enhance local neighborhood geometry encoding. For
the point branch, we use Multi-Layer Perceptron (MLP) to extract fine-detailed
point-wise features. Outputs from these two branches are adaptively fused via a
feature selection module. Moreover, we supervise the output from every PVC
layer to learn different levels of semantic information. The final prediction
is made by averaging all intermediate predictions. We demonstrate empirically
that our method is able to achieve comparable results while being fast and
memory efficient. We evaluate our method on popular point cloud datasets for
object classification and semantic segmentation tasks.
