论文标题
完整和标签：liDar点云语义分割的域适应方法
Complete & Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Clouds
论文作者
论文摘要
我们研究了3D点云语义标记的无监督域的适应问题，特别关注不同激光雷达传感器引起的域差异。基于从3D表面采样稀疏3D点云的观察结果，我们采用完整的标签方法来恢复基础表面，然后再将其传递到分割网络。具体来说，我们设计了一个稀疏的体素完成网络（SVCN）来完成稀疏点云的3D表面。与语义标签不同，要获得SVCN的训练对，不需要手动标签。我们还引入了局部对抗性学习，以对表面进行建模。回收的3D表面充当一个规范结构域，语义标签可以从中传递不同的LiDAR传感器。通过我们的新基准测试对LIDAR数据的跨域语义标记的实验和消融研究表明，所提出的方法比以前的域适应方法可提供8.2-36.6％的性能。
We study an unsupervised domain adaptation problem for the semantic labeling of 3D point clouds, with a particular focus on domain discrepancies induced by different LiDAR sensors. Based on the observation that sparse 3D point clouds are sampled from 3D surfaces, we take a Complete and Label approach to recover the underlying surfaces before passing them to a segmentation network. Specifically, we design a Sparse Voxel Completion Network (SVCN) to complete the 3D surfaces of a sparse point cloud. Unlike semantic labels, to obtain training pairs for SVCN requires no manual labeling. We also introduce local adversarial learning to model the surface prior. The recovered 3D surfaces serve as a canonical domain, from which semantic labels can transfer across different LiDAR sensors. Experiments and ablation studies with our new benchmark for cross-domain semantic labeling of LiDAR data show that the proposed approach provides 8.2-36.6% better performance than previous domain adaptation methods.
