Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds
  Semantic Segmentation   The need for fine-grained perception in autonomous driving systems has
resulted in recently increased research on online semantic segmentation of
single-scan LiDAR. Despite the emerging datasets and technological
advancements, it remains challenging due to three reasons: (1) the need for
near-real-time latency with limited hardware; (2) uneven or even long-tailed
distribution of LiDAR points across space; and (3) an increasing number of
extremely fine-grained semantic classes. In an attempt to jointly tackle all
the aforementioned challenges, we propose a new LiDAR-specific,
nearest-neighbor-free segmentation algorithm - PolarNet. Instead of using
common spherical or bird's-eye-view projection, our polar bird's-eye-view
representation balances the points across grid cells in a polar coordinate
system, indirectly aligning a segmentation network's attention with the
long-tailed distribution of the points along the radial axis. We find that our
encoding scheme greatly increases the mIoU in three drastically different
segmentation datasets of real urban LiDAR single scans while retaining near
real-time throughput.

29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex
35
The categories of this paper are: mtrl-th
37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
19
The categories of this paper are: eess
4
The categories of this paper are: cond-mat