论文标题
3D-Mininet：从点云中学习2D表示，以快速有效3D LIDAR语义分段
3D-MiniNet: Learning a 2D Representation from Point Clouds for Fast and Efficient 3D LIDAR Semantic Segmentation
论文作者
论文摘要
LiDAR语义分段为LIDAR测量的每个3D点分配了语义标签，它已成为许多机器人应用（例如自主驾驶）的重要任务。需要快速有效的语义分割方法来匹配许多这些现实世界应用的强大计算和时间限制。 这项工作介绍了3D-Mininet，这是一种新颖的LIDAR语义分割方法，结合了3D和2D学习层。它首先通过新的投影从原始点学习了2D表示，该预测从3D数据中提取本地和全局信息。该表示形式被馈送到产生2D语义分割的有效2D完全卷积神经网络（FCNN）。这些2D语义标签被重新投影回3D空间，并通过后处理模块增强。我们策略中的主要新颖性依赖于投射学习模块。我们的详细消融研究表明，每个组件如何促进3D分钟的最终性能。我们验证了我们在众所周知的公共基准（Semantickitti和Kitti）上的方法，其中3D-Mininet获得了最先进的结果，同时比以前的方法更快，更有效率。
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.
