摘要：

随着自动驾驶和智能城市的快速发展, 精确的道路点云分割和重建成为关键技术需求。为解决传统分割方法在处理复杂道路几何形态时的局限性, 并充分发挥倾斜摄影技术快速生成真实场景点云模型的优势, 提出一种新的分割优化方法。该方法通过引入连通性指标指导的自动化调优策略, 结合基于法向量、曲率等特征的局部编码模块, 增强语义分割网络捕捉复杂道路几何特征的能力, 从而提高分割精度, 并增强道路结构的整体连续性。同时, 提出一种基于道路中线提取与修复的迭代分割优化策略, 一方面将补足倾斜摄影点云无法避免的空洞点云问题, 转换为道路中线的识别及修复、道路宽度估算等相对易解决的问题, 在此基础上, 优化现有道路中线提取算法, 解决传统算法存在的道路变形、不完整等问题; 另一方面, 创新性地将道路中线及道路宽度作为先验知识, 辅助点云语义分割模型进行更高精度的道路提取。最后, 设计一整套道路程序化生成的流程方案, 可用于自动稳定地复原具有不同类型路口的复杂道路模型。实验结果表明, 所提方法具有较优的重建效果, 为道路点云处理技术的进步提供了新的思路与应用价值。

关键词: 点云语义分割, 拓扑连通性, 贝叶斯优化, 道路中线提取, 道路宽度估算, 程序化生成

Abstract:

With the rapid development of autonomous driving and smart cities, accurate road point cloud segmentation and reconstruction have become key technological requirements. Traditional segmentation methods face difficulties when dealing with complex road geometries. To address this issue and maximize the advantages of oblique photography technology to quickly generate a real-field scenic spot cloud model, this study proposes a new segmentation optimization method. This method introduces an automatic optimization strategy guided by the connectivity evaluation index and combines a local coding module based on the normal vector, curvature, and other features. Consequently, the ability of the semantic segmentation network to capture complex road geometric features is enhanced, thereby improving the segmentation accuracy and enhancing the overall continuity of road structures. In addition, the study proposes an iterative segmentation optimization strategy based on road median line extraction and repair. On the one hand, the problem of compensating unavoidable hollow point clouds in oblique photographic point clouds is transformed into easy problems such as road median line identification and repair and road width estimation, and on this basis, the existing road median line extraction algorithm is optimized. The problems of road deformation and incompleteness are solved using traditional algorithms. On the other hand, the road median and road width are innovatively taken as prior knowledge to assist the point cloud semantic segmentation model in extracting roads with higher accuracy. Finally, a complete set of procedurally generated road-flow schemes is implemented, which can be used to automate and stably restore complex road models with different types of intersections. Experimental results verify that the proposed method significantly improves the reconstruction effect, providing a new concept and application value for the progress of road point cloud processing technology.

Key words: point cloud segmentation, topological connectivity, Bayesian Optimization (BO), road midline extraction, road width estimation, programmed generation