  Semantic segmentation of 3D point cloud is an essential task for autonomous
driving environment perception. The pipeline of most pointwise point cloud
semantic segmentation methods includes points sampling, neighbor searching,
feature aggregation, and classification. Neighbor searching method like
K-nearest neighbors algorithm, KNN, has been widely applied. However, the
complexity of KNN is always a bottleneck of efficiency. In this paper, we
propose an end-to-end neural architecture, Multiple View Pointwise Net,
MVP-Net, to efficiently and directly infer large-scale outdoor point cloud
without KNN or any complex pre/postprocessing. Instead, assumption-based space
filling curves and multi-rotation of point cloud methods are introduced to
point feature aggregation and receptive field expanding. Numerical experiments
show that the proposed MVP-Net is 11 times faster than the most efficient
pointwise semantic segmentation method RandLA-Net and achieves the same
accuracy on the large-scale benchmark SemanticKITTI dataset.
