基于PointNet+的道路场景中的点云数据的分类
Jingfeng Xue1, Bin Zhao2, Chunhong Zhao2
1Qingdao Huanghai University, Qingdao 266555, China.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
本研究引入了深度学习方法,用于在城市道路场景中自动化点云分类. 增强的PointNet++模型在识别道路基础设施方面实现了高精度,改善了测量和管理.
科学领域:
- 地理信息学是一种地理信息学.
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 点云数据对于城市道路基础设施管理至关重要.
- 手动对点云数据进行分类是耗时且容易出现错误的.
- 深度学习为自动化特征提取和分类提供了潜力.
研究的目的:
- 开发一种自动化的深度学习方法,用于道路场景中的点云分类.
- 增强PointNet++框架,以提高准确性和稳定性.
- 为培训和评估道路场景点云模型创建一个全面的数据集.
主要方法:
- 使用了普林斯顿模型Net40,ShapeNet和悉尼城市对象数据集.
- 实现最远点采样 (FPS),随机转换和高斯噪声注入以增强数据.
- 将点填充方法集成到PointNet++预处理模块中.
- 雇员多级分组 (MSG) 和单级分组 (SSG) 方案用于模型培训和预测.
主要成果:
- 实现了86.26%的平均训练准确率,最高单个实例准确率为98.54%.
- 达到97.41%的测试集精度,类精度为84.50%.
- 在复杂的道路环境中展示了高精度的对象识别.
结论:
- 开发的深度学习模型成功地对道路场景点云进行了分类.
- 这项研究为城市基础设施的点云数据处理提供了宝贵的见解.
- 自动分类提高了道路测量和管理的效率和精度.
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