  LiDAR sensor is essential to the perception system in autonomous vehicles and
intelligent robots. To fulfill the real-time requirements in real-world
applications, it is necessary to efficiently segment the LiDAR scans. Most of
previous approaches directly project 3D point cloud onto the 2D spherical range
image so that they can make use of the efficient 2D convolutional operations
for image segmentation. Although having achieved the encouraging results, the
neighborhood information is not well-preserved in the spherical projection.
Moreover, the temporal information is not taken into consideration in the
single scan segmentation task. To tackle these problems, we propose a novel
approach to semantic segmentation for LiDAR sequences named Meta-RangeSeg,
where a new range residual image representation is introduced to capture the
spatial-temporal information. Specifically, Meta-Kernel is employed to extract
the meta features, which reduces the inconsistency between the 2D range image
coordinates input and 3D Cartesian coordinates output. An efficient U-Net
backbone is used to obtain the multi-scale features. Furthermore, Feature
Aggregation Module (FAM) strengthens the role of range channel and aggregates
features at different levels. We have conducted extensive experiments for
performance evaluation on SemanticKITTI and SemanticPOSS. The promising results
show that our proposed Meta-RangeSeg method is more efficient and effective
than the existing approaches. Our full implementation is publicly available at
https://github.com/songw-zju/Meta-RangeSeg .
