论文标题
通过学习特征在互补表示方面通过学习功能来改善基于激光雷达的语义分割
Improving Lidar-Based Semantic Segmentation of Top-View Grid Maps by Learning Features in Complementary Representations
论文作者
论文摘要
在本文中，我们介绍了一种新颖的方式，可以在自主驾驶的背景下从稀疏的单发痛苦测量中预测语义信息。特别是，我们融合了从互补表示中学习的功能。该方法专门用于改善顶级网格图的语义分割。为了实现这一目标，3D激光点云将投影到两个正交2D表示。对于每种表示形式，都开发了一个量身定制的深度学习体系结构，以有效提取由上级深神经网络融合的语义信息。这项工作的贡献是三重的：（1）我们检查了分割网络中的不同阶段的融合阶段。 （2）我们量化了嵌入不同特征的影响。 （3）我们使用此调查的发现来设计量身定制的深神网络体系结构，以利用不同表示的各个优势。使用Semantickitti数据集对我们的方法进行评估，该数据集提供了超过23.000 LIDAR测量的点语义注释。
In this paper we introduce a novel way to predict semantic information from sparse, single-shot LiDAR measurements in the context of autonomous driving. In particular, we fuse learned features from complementary representations. The approach is aimed specifically at improving the semantic segmentation of top-view grid maps. Towards this goal the 3D LiDAR point cloud is projected onto two orthogonal 2D representations. For each representation a tailored deep learning architecture is developed to effectively extract semantic information which are fused by a superordinate deep neural network. The contribution of this work is threefold: (1) We examine different stages within the segmentation network for fusion. (2) We quantify the impact of embedding different features. (3) We use the findings of this survey to design a tailored deep neural network architecture leveraging respective advantages of different representations. Our method is evaluated using the SemanticKITTI dataset which provides a point-wise semantic annotation of more than 23.000 LiDAR measurements.
