Semantic Segmentation of 3D Scene based on Global Feature Fusion

Semantic segmentation is an important tool for computers to perceive the real world and it is the basis and key to solve high-level vision tasks. However, as the real scene contains a large number of complex objects and is also affected by factors such as occlusion, the performance of the segmentation method based on single-modal data is affected. In order to improve the accuracy of segmentation and reduce the influence of object occlusion, truncation and other factors, a 3D scene semantic segmentation framework based on 2D image features, geometric structure and global context information is proposed. It adopts a heterogeneous network to combine image and depth information effectively, thus solving the problem that the result of using single-modal data is not fine enough. Experimental results show that the proposed method is effective in understanding complex scenes.