Semantic Segmentation of Indoor 3D Point Cloud Model Based on 2D-3D Semantic Transfer

In this paper, we propose an effective algorithm based on graph model for semantic transfer from 2D images to 3D point clouds, which can effectively solve the problem of objectification and lack of structured information of 3D point cloud model. Our proposed method uses the extended full convolutional neural network to extract the indoor space layout and object semantics of 2D images, and then implements the transfer of 2D semantics to 3D semantics based on the 2D image superpixels and 3D point clouds as nodes to construct a graph model of consistency between images and intra-image consistency. The experiment from 3D point cloud shows that the proposed method can obtain accurate indoor 3D point cloud semantic classification results. The accuracy of point cloud classification can reach 73.875 2%, and the classification effect is better.