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
this https URL

我们提出了一种端到端的语义分割辅助场景补全网络，包括 2D 补全分支和 3D 语义分割分支，以提供语义信息。通过采用 BEV 表示和 3D 稀疏卷积，我们可以从较低的操作数中受益，同时保持有效的表达。此方法在 SemanticKITTI 数据集上表现出有竞争力的性能和低延迟。