Indoor Point Cloud Segmentation Based on Hierarchical Fusion of Structural Features

Aiming at the problem that the existing algorithms are difficult to segment efficiently in indoor scenes due to the high similarity of stacked and closely adjacent objects, this paper proposes an indoor point cloud segmentation algorithm based on hierarchical feature fusion. Firstly, according to the results of stratification, the principal component analysis method is adopted to reduce the dimension of each layer point clouds, and then the decision rules on the two-dimensional plane are established based on the structural characteristics of each layer objects. Combined with the analysis of hierarchical connectivity, fusing the features of each layer, so as to obtain the global positioning of the object. Last, for further division, the density segmentation method based on inter cluster constraints are taken into consideration. The experimental results show that the location and segmentation effect of stacked objects is fine, it is worth mentioning that this method has high accuracy, and the average mIoU can reach 0.811. In addition, another advantage of this method is that it takes less time to process point clouds, the average consumption time is about 6.59 seconds. These characteristics represent that this method can better complete the segmentation of indoor scenes.