基于改进超体素与图割的室内场景点云分割

Indoor scene point cloud segmentation based on improved supervoxel and graph cut

室内场景点云分割是三维场景感知、理解、分析及应用的基础。尽管目前的室内点云分割方法可以应用于很多场景,但缺乏对不同结构分割的适应性,在处理临近平行面的分割时,仍无法避免欠分割,因此,本文提出了一种基于改进超体素与图割的方法。首先通过对超体素的邻域选择、法向量计算、特征距离度量,以及对超体素局部邻接图的空间连通性约束与分割,构建出自适应处理临近平行面关系的机制,实现复杂室内场景点云的有效分割,克服欠分割现象;最后通过4组室内场景点云进行验证,并与现有方法进行对比分析。结果表明,本文方法提高了复杂室内场景点云分割的精确率和召回率,验证了其对不同结构分割的适应性和有效性。

Indoor scene point cloud segmentation is the basis for the perception,understanding,analysis and application of 3D scenes.Although the current indoor point cloud segmentation method can be applied to many scenes,it lacks the ability to adapt to the segmentation of different structures,and the bottleneck of undersegmentation cannot be avoided when dealing with segmentation of near parallel surfaces.Therefore,a method based on improved supervoxel and graph cut is proposed,through the neighborhood selection,normal vector calculation,feature distance measurement,and spatial connectivity constraint and segmentation of supervoxel local adjacency graph,an adaptive mechanism for processing the relationship between adjacent parallel surfaces is constructed,which realizes the effective segmentation of point cloud in complex indoor scenes,and the above-mentioned undersegmentation phenomenon is overcome.Finally,the proposed method is verified by four indoor scene point clouds,and compared with the existing methods.Experimental results show that the proposed method improves the accuracy and recall of point cloud segmentation in complex indoor scenes,and verifies the adaptability and effectiveness of the method to different structural segmentation.