论文标题
LIDARNET：点云语义分割的边界感知域的适应模型
LiDARNet: A Boundary-Aware Domain Adaptation Model for Point Cloud Semantic Segmentation
论文作者
论文摘要
我们提出了激光扫描全景扫描语义分割（Lidarnet）的边界感知域的适应模型。我们的模型可以提取域私有功能和具有两分支结构的域共享特征。我们将门控SCNN嵌入了Lidarnet的分段组件中，以学习边界信息，同时学习预测全景语义分段标签。此外，我们通过诱导模型使用共享域和私有功能来学习两个域之间的映射，从而进一步减少域间隙。此外，我们介绍了一个新的数据集（semanticusl \ footNote {semanticusl的访问地址：\ url {https://unmannedlab.github.github.io/research/semanticusl}}）用于域适应激光点云semantic semantic semantic semantic semantic semantic semantation。该数据集具有与Semantickitti相同的数据格式和本体学。我们对现实世界数据集Semantickitti，semanticposs和semanticusl进行了实验，这些实验在信道分布，反射率分布，场景多样性和传感器设置方面存在差异。使用我们的方法，我们可以获得一个基于投影的LiDAR全景语义分割模型，可在两个域上工作。我们的模型在适应后可以保持几乎相同的性能，并在目标域中获得8 \％-22 \％MIOU性能的提高。
We present a boundary-aware domain adaptation model for LiDAR scan full-scene semantic segmentation (LiDARNet). Our model can extract both the domain private features and the domain shared features with a two-branch structure. We embedded Gated-SCNN into the segmentor component of LiDARNet to learn boundary information while learning to predict full-scene semantic segmentation labels. Moreover, we further reduce the domain gap by inducing the model to learn a mapping between two domains using the domain shared and private features. Additionally, we introduce a new dataset (SemanticUSL\footnote{The access address of SemanticUSL:\url{https://unmannedlab.github.io/research/SemanticUSL}}) for domain adaptation for LiDAR point cloud semantic segmentation. The dataset has the same data format and ontology as SemanticKITTI. We conducted experiments on real-world datasets SemanticKITTI, SemanticPOSS, and SemanticUSL, which have differences in channel distributions, reflectivity distributions, diversity of scenes, and sensors setup. Using our approach, we can get a single projection-based LiDAR full-scene semantic segmentation model working on both domains. Our model can keep almost the same performance on the source domain after adaptation and get an 8\%-22\% mIoU performance increase in the target domain.
