针对铁路场景优化点云分割，通过数据增强提升远距离识别精度。
A Data-Centric Approach to 3D Semantic Segmentation of Railway Scenes
- 引入行人实例贴入和轨道稀疏化两种数据增强方法
- 远距离行人与轨道分割准确率显著提升，近距性能保持稳定
- 为铁路场景构建首个3D语义分割基准，适合自动驾驶列车研究者
基于激光雷达的语义分割对自动驾驶列车至关重要，需在不同距离下实现精准预测。本文提出两种面向铁路场景的数据增强方法，用于提升OSDaR23数据集上的分割性能。行人实例贴入法通过向数据集注入真实变化，增强远距离行人分割能力；轨道稀疏化法重新分布点云密度，提升远距离轨道分割效果，同时对近距离精度影响极小。两种方法在当前最先进的3D语义分割网络上验证，显著改善远距离表现，且保持近距预测鲁棒性。本文建立了OSDaR23首个3D语义分割基准，展示了数据驱动方法在解决铁路自动驾驶感知挑战中的潜力。
原文摘要 · Abstract (English)
LiDAR-based semantic segmentation is critical for autonomous trains, requiring accurate predictions across varying distances. This paper introduces two targeted data augmentation methods designed to improve segmentation performance on the railway-specific OSDaR23 dataset. The person instance pasting method enhances segmentation of pedestrians at distant ranges by injecting realistic variations into the dataset. The track sparsification method redistributes point density in LiDAR scans, improving track segmentation at far distances with minimal impact on close-range accuracy. Both methods are evaluated using a state-of-the-art 3D semantic segmentation network, demonstrating significant improvements in distant-range performance while maintaining robustness in close-range predictions. We establish the first 3D semantic segmentation benchmark for OSDaR23, demonstrating the potential of data-centric approaches to address railway-specific challenges in autonomous train perception.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。