论文标题
重新思考3D激光雷德点云分段
Rethinking 3D LiDAR Point Cloud Segmentation
论文作者
论文摘要
许多基于点的语义分割方法都是为室内场景而设计的，但是如果将它们应用于室外环境中的激光雷达传感器捕获的点云，则它们会挣扎。为了使这些方法更有效，更健壮，以便它们可以处理LiDAR数据，我们介绍了重新设计基于3D点的操作的一般概念，以便它们可以在投影空间中运行。尽管我们通过三种基于三点的方法表明，重新制定版本的速度更快300到400倍，并且可以实现更高的准确性，但我们此外表明，重新设计基于3D点的操作的概念允许设计新的架构，以统一基于点和基于图像的方法的好处。例如，我们介绍了一个将基于3D点的操作集成到2D编码器架构中的网络，该操作融合了来自不同2D量表的信息。我们在四个具有挑战性的数据集上评估了用于语义激光雷德点云进行分割的方法，并表明利用基于2D的基于2D图像的操作的基于重新重新的基于3D点的操作为所有四个数据集都取得了很好的结果。
Many point-based semantic segmentation methods have been designed for indoor scenarios, but they struggle if they are applied to point clouds that are captured by a LiDAR sensor in an outdoor environment. In order to make these methods more efficient and robust such that they can handle LiDAR data, we introduce the general concept of reformulating 3D point-based operations such that they can operate in the projection space. While we show by means of three point-based methods that the reformulated versions are between 300 and 400 times faster and achieve a higher accuracy, we furthermore demonstrate that the concept of reformulating 3D point-based operations allows to design new architectures that unify the benefits of point-based and image-based methods. As an example, we introduce a network that integrates reformulated 3D point-based operations into a 2D encoder-decoder architecture that fuses the information from different 2D scales. We evaluate the approach on four challenging datasets for semantic LiDAR point cloud segmentation and show that leveraging reformulated 3D point-based operations with 2D image-based operations achieves very good results for all four datasets.
