Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MVP-Net: Multiple View Pointwise Semantic Segmentation of Large-Scale
  Point Clouds   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.

25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
16
The categories of this paper are: stat
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th
22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an