铁路点云的时空建模：一种基于 Mamba 的高效三维框架与新基准
Spatial-Temporal Modeling for Railway Point Cloud: An Efficient 3D Framework with Mamba and A New Benchmark
刘兆轩 1黄雅平1
作者信息
- 1. 北京交通大学计算机科学与技术学院，北京，100044
- 折叠
摘要
三维点云语义分割在场景理解中发挥着重要作用，并已广泛应用于自动驾驶和增强现实等各种下游任务中。本文主要关注铁路场景中的点云分割问题，该领域在以往的研究中仍未得到充分探索。首先，本文精心构建了一个名为Railway360的铁路点云数据集，该数据集采集自中国的一条主要铁路线，覆盖4.1公里铁路，包含约8亿个点，并被划分为10个类别。得益于单线高频激光雷达的使用以及特定的垂直扫描模式，Railway360不仅提供了位置坐标、强度信息和类别标签，还记录了点云采集的时间戳，使其能够提供连续、完整且均匀采样的点云数据，从而能够对场景进行更精确的时间分析，这将极大地促进场景分割和轨道设备测量等后续任务。其次，本文提出了一种名为RailSeg-Mamba的基于Mamba架构的新型铁路三维分割框架，该框架利用选择性状态空间模型在序列特征建模方面的强大能力，实现了空间和时间特征的充分融合。本文对最先进的方法进行了广泛的性能评估，并在我们的数据集上建立了一个新的基准。实验结果表明，将点云建模为序列为铁路分割提供了一种极具前景的解决方案。
Abstract
3D point cloud semantic segmentation plays a significant role in scene understanding and has been widely applied in various downstream tasks, such as autonomous driving and augmented reality. In this paper, we focus on point cloud segmentation on railway scenarios that remains underexplored in previous studies. First, we meticulously construct a railway point cloud dataset, Railway360, which is collected from a main railway line in China, covering 4.1 kilometers and approximately 800 million points divided into 10 categories. Thanks to the usage of a single-line high-frequency LiDAR and the specific vertical scanning pattern, Railway360 provides not only the position coordinates, intensities, and category labels, but also the timestamps of the point cloud collection. This allows us to obtain a continuously complete and uniformly sampled point cloud dataset, enabling more accurate temporal analysis of the scene, which can largely benefit subsequent tasks such as scene segmentation and track equipment measurement. Second, we propose a novel Mamba-based framework, named RailSeg-Mamba, for railway 3D segmentation, where spatial and temporal features are fully integrated by leveraging the significant capabilities of modeling the sequence features of the selective state space models. To our knowledge, this is the first framework that takes the temporal features of point cloud into account. We extensively evaluate the performance of the state-of-the-art methods and provide a new benchmark on our dataset. The results demonstrate that modeling point clouds as sequences provides a promising solution for railway segmentation.关键词
计算机应用技术/深度学习/三维点云Key words
Computer Application Technology/Deep Learning/3D Point Cloud引用本文复制引用
刘兆轩,黄雅平.铁路点云的时空建模：一种基于 Mamba 的高效三维框架与新基准[EB/OL].(2026-03-19)[2026-03-21].http://www.paper.edu.cn/releasepaper/content/202603-181.学科分类
铁路运输工程/计算技术、计算机技术
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