  Outdoor scene completion is a challenging issue in 3D scene understanding,
which plays an important role in intelligent robotics and autonomous driving.
Due to the sparsity of LiDAR acquisition, it is far more complex for 3D scene
completion and semantic segmentation. Since semantic features can provide
constraints and semantic priors for completion tasks, the relationship between
them is worth exploring. Therefore, we propose an end-to-end semantic
segmentation-assisted scene completion network, including a 2D completion
branch and a 3D semantic segmentation branch. Specifically, the network takes a
raw point cloud as input, and merges the features from the segmentation branch
into the completion branch hierarchically to provide semantic information. By
adopting BEV representation and 3D sparse convolution, we can benefit from the
lower operand while maintaining effective expression. Besides, the decoder of
the segmentation branch is used as an auxiliary, which can be discarded in the
inference stage to save computational consumption. Extensive experiments
demonstrate that our method achieves competitive performance on SemanticKITTI
dataset with low latency. Code and models will be released at
https://github.com/jokester-zzz/SSA-SC.
