  We present a new convolutional neural network, called Multi Voxel-Point
Neurons Convolution (MVPConv), for fast and accurate 3D deep learning. The
previous works adopt either individual point-based features or
local-neighboring voxel-based features to process 3D model, which limits the
performance of models due to the inefficient computation. Moreover, most of the
existing 3D deep learning frameworks aim at solving one specific task, and only
a few of them can handle a variety of tasks. Integrating both the advantages of
the voxel and point-based methods, the proposed MVPConv can effectively
increase the neighboring collection between point-based features and also
promote the independence among voxel-based features. Simply replacing the
corresponding convolution module with MVPConv, we show that MVPConv can fit in
different backbones to solve a wide range of 3D tasks. Extensive experiments on
benchmark datasets such as ShapeNet Part, S3DIS and KITTI for various tasks
show that MVPConv improves the accuracy of the backbone (PointNet) by up to
36%, and achieves higher accuracy than the voxel-based model with up to 34
times speedup. In addition, MVPConv also outperforms the state-of-the-art
point-based models with up to 8 times speedup. Notably, our MVPConv achieves
better accuracy than the newest point-voxel-based model PVCNN (a model more
efficient than PointNet) with lower latency.
