  LiDAR semantic segmentation essential for advanced autonomous driving is
required to be accurate, fast, and easy-deployed on mobile platforms. Previous
point-based or sparse voxel-based methods are far away from real-time
applications since time-consuming neighbor searching or sparse 3D convolution
are employed. Recent 2D projection-based methods, including range view and
multi-view fusion, can run in real time, but suffer from lower accuracy due to
information loss during the 2D projection. Besides, to improve the performance,
previous methods usually adopt test time augmentation (TTA), which further
slows down the inference process. To achieve a better speed-accuracy trade-off,
we propose Cascade Point-Grid Fusion Network (CPGNet), which ensures both
effectiveness and efficiency mainly by the following two techniques: 1) the
novel Point-Grid (PG) fusion block extracts semantic features mainly on the 2D
projected grid for efficiency, while summarizes both 2D and 3D features on 3D
point for minimal information loss; 2) the proposed transformation consistency
loss narrows the gap between the single-time model inference and TTA. The
experiments on the SemanticKITTI and nuScenes benchmarks demonstrate that the
CPGNet without ensemble models or TTA is comparable with the state-of-the-art
RPVNet, while it runs 4.7 times faster.
