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The need for fine-grained perception in autonomous driving systems hasresulted in recently increased research on online semantic segmentation ofsingle-scan LiDAR. Despite the emerging datasets and technologicaladvancements, it remains challenging due to three reasons: (1) the need fornear-real-time latency with limited hardware; (2) uneven or even long-taileddistribution of LiDAR points across space; and (3) an increasing number ofextremely fine-grained semantic classes. In an attempt to jointly tackle allthe aforementioned challenges, we propose a new LiDAR-specific,nearest-neighbor-free segmentation algorithm - PolarNet. Instead of usingcommon spherical or bird's-eye-view projection, our polar bird's-eye-viewrepresentation balances the points across grid cells in a polar coordinatesystem, indirectly aligning a segmentation network's attention with thelong-tailed distribution of the points along the radial axis. We find that ourencoding scheme greatly increases the mIoU in three drastically differentsegmentation datasets of real urban LiDAR single scans while retaining nearreal-time throughput.

PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation