  Semantic Segmentation is a crucial component in the perception systems of
many applications, such as robotics and autonomous driving that rely on
accurate environmental perception and understanding. In literature, several
approaches are introduced to attempt LiDAR semantic segmentation task, such as
projection-based (range-view or birds-eye-view), and voxel-based approaches.
However, they either abandon the valuable 3D topology and geometric relations
and suffer from information loss introduced in the projection process or are
inefficient. Therefore, there is a need for accurate models capable of
processing the 3D driving-scene point cloud in 3D space. In this paper, we
propose S3Net, a novel convolutional neural network for LiDAR point cloud
semantic segmentation. It adopts an encoder-decoder backbone that consists of
Sparse Intra-channel Attention Module (SIntraAM), and Sparse Inter-channel
Attention Module (SInterAM) to emphasize the fine details of both within each
feature map and among nearby feature maps. To extract the global contexts in
deeper layers, we introduce Sparse Residual Tower based upon sparse convolution
that suits varying sparsity of LiDAR point cloud. In addition, geo-aware
anisotrophic loss is leveraged to emphasize the semantic boundaries and
penalize the noise within each predicted regions, leading to a robust
prediction. Our experimental results show that the proposed method leads to a
large improvement (12\%) compared to its baseline counterpart (MinkNet42
\cite{choy20194d}) on SemanticKITTI \cite{DBLP:conf/iccv/BehleyGMQBSG19} test
set and achieves state-of-the-art mIoU accuracy of semantic segmentation
approaches.
