3D点云的语义细分和效果优化基于2D语义细分和集群用于建筑机械非结构化的环境
Shengjie Fu1,2, Qipeng Cai1, Zhongshen Li1,2
1College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China.
Sensors (Basel, Switzerland)
|February 27, 2026
概括
本研究介绍了建筑3D语义感知的新方法,使用2D图像标签来理解复杂的场景,而无需昂贵的3D数据. 这种方法提高了建筑机械的性能.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 地理空间科学 地理空间科学
背景情况:
- 建筑机械在非结构化的环境中运行,具有复杂,动态的场景.
- 3D语义感知至关重要,但受到3D点云标签的高成本的挑战.
- 现有的方法与3D数据注释相关的复杂性和成本作斗争.
研究的目的:
- 为非结构化建筑环境开发一种新的3D语义感知方案.
- 为了实现准确的3D理解,只使用2D图像标签,减少对3D注释的依赖.
- 通过增强感知来提高建筑机械的运行效率和安全性.
主要方法:
- 整合2D图像语义细分与3D点云集群通过透视投影.
- 使用粒子集群优化 (PSO) 改进投影参数.
- 使用基于Kd树的半径近邻 (RNN) 匹配算法增强语义一致性.
主要成果:
- 在没有注释的3D点云的情况下,实现准确的3D语义理解的弱监督框架.
- 成功感知3D语义信息和重建目标轮.
- 实现了平均像素精度 (mPA) 的84.72%和平均交叉超过欧盟 (mIoU) 的75.85%.
结论:
- 拟议的方案有效地解决了在非结构化环境中的3D语义感知挑战.
- 通过专用数据集和现实世界的测试来验证可行性和有效性.
- 展示了在建筑中的3D语义理解的成本效益和准确方法.
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