LIDAR semantic segmentation, which assigns a semantic label to each 3D point
measured by the LIDAR, is becoming an essential task for many robotic
applications such as autonomous driving. Fast and efficient semantic
segmentation methods are needed to match the strong computational and temporal
restrictions of many of these real-world applications.
This work presents 3D-MiniNet, a novel approach for LIDAR semantic
segmentation that combines 3D and 2D learning layers. It first learns a 2D
representation from the raw points through a novel projection which extracts
local and global information from the 3D data. This representation is fed to an
efficient 2D Fully Convolutional Neural Network (FCNN) that produces a 2D
semantic segmentation. These 2D semantic labels are re-projected back to the 3D
space and enhanced through a post-processing module. The main novelty in our
strategy relies on the projection learning module. Our detailed ablation study
shows how each component contributes to the final performance of 3D-MiniNet. We
validate our approach on well known public benchmarks (SemanticKITTI and
KITTI), where 3D-MiniNet gets state-of-the-art results while being faster and
more parameter-efficient than previous methods.

本研究提出了一种新的 LIDAR 语义分割方法，称为 3D-MiniNet，该方法结合了 3D 和 2D 学习层。该方法利用投影学习模块提取 3D 数据的局部和全局信息，并通过后处理模块将 2D 语义标签重新投影到 3D 空间中，在 SemanticKITTI 和 KITTI 公共基准测试中表现出比以前的方法更快、更节约参数的优势，并得到了最先进的结果。