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.

本文提出了针对 LiDAR 数据的新型自动驾驶车辆的语义分割算法 PolarNet，通过極坐标系间接对齐分割网络的注意力，解决了点云分布不均等问题。在三个不同的真实城市 LiDAR 单次扫描数据集中，该编码方案显著提高了平均交并比，同时保持了接近实时的吞吐量。