To address the current issues of inaccurate segmentation and the limited applicability of segmentation methods for building facades in point clouds, we propose a facade segmentation algorithm based on optimal dual-sca...To address the current issues of inaccurate segmentation and the limited applicability of segmentation methods for building facades in point clouds, we propose a facade segmentation algorithm based on optimal dual-scale feature descriptors. First, we select the optimal dual-scale descriptors from a range of feature descriptors. Next, we segment the facade according to the threshold value of the chosen optimal dual-scale descriptors. Finally, we use RANSAC (Random Sample Consensus) to fit the segmented surface and optimize the fitting result. Experimental results show that, compared to commonly used facade segmentation algorithms, the proposed method yields more accurate segmentation results, providing a robust data foundation for subsequent 3D model reconstruction of buildings.展开更多
针对目前已有的手工设计描述符对局部曲面几何特征描述不够全面的问题,本文提出了一种高鉴别强鲁棒的多视图几何分布特征描述符(Multi-View Geometric Distribution Signatures,MGDS)。首先,基于关键点及其邻域点构建局部参考框架(Local...针对目前已有的手工设计描述符对局部曲面几何特征描述不够全面的问题,本文提出了一种高鉴别强鲁棒的多视图几何分布特征描述符(Multi-View Geometric Distribution Signatures,MGDS)。首先,基于关键点及其邻域点构建局部参考框架(Local Reference Frame,LRF),对局部曲面进行体素化,计算三维体素的质心点分布、二维扇区轮廓特征、二维网格点密度分布以及局面曲面深度波动,生成几何特征描述符。接着,基于LRF多次旋转局部曲面,产生新的形状表示,利用质心、轮廓点、密度以及z值波动信息对旋转后的曲面进行编码。通过多个视角获取这些几何特征描述符,将它们串联成一个特征向量,得到最终的多视图几何分布特征描述符MGDS。本文在RandomView,SpaceTime,Kinect以及B3R四个数据集中不同的高斯噪声以及网格分辨率下进行实验,并与目前已有的10种描述符进行比较。与其他描述符相比,MGDS描述符的性能优于已有的局部特征描述符。实验结果表明,本文所提出的MGDS具有较好的描述性与鲁棒性,可用于三维点云的准确配准。展开更多
文摘To address the current issues of inaccurate segmentation and the limited applicability of segmentation methods for building facades in point clouds, we propose a facade segmentation algorithm based on optimal dual-scale feature descriptors. First, we select the optimal dual-scale descriptors from a range of feature descriptors. Next, we segment the facade according to the threshold value of the chosen optimal dual-scale descriptors. Finally, we use RANSAC (Random Sample Consensus) to fit the segmented surface and optimize the fitting result. Experimental results show that, compared to commonly used facade segmentation algorithms, the proposed method yields more accurate segmentation results, providing a robust data foundation for subsequent 3D model reconstruction of buildings.
