DCFNet：Dual-Channel Feature Fusion of Real Scene for Point Cloud Semantic Segmentation

The point cloud of the real scene not only has the spatial geometric information of the point cloud, but also has the color information of the 3D object. The existing network cannot effectively use the local features and spatial geometric feature information of the real scene. Therefore, a dual-channel feature fusion of real scene for point cloud semantic segmentation DCFNet can be used for indoor and outdoor scene semantic segmentation in different scenarios. More specifically, in order to solve the problem that the color information of the point cloud in the real scene cannot be fully extracted, the method uses two input channels, and the channel adopts the same feature extraction network structure. The input of the upper channel is the complete RGB color and point cloud coordinate information, and the channel mainly focuses on the scene features of complex objects, while the lower channel only inputs the point cloud coordinate information. This channel mainly focuses on the spatial geometric characteristics of the point cloud. In each channel, in order to better extract local and global information and improve network performance, the inter-layer fusion module and the Transformer channel feature expansion module are introduced. At the same time, the existing 3D point cloud semantic segmentation methods lack of attention to the relationship between local features and global features, which leads to poor segmentation results for complex scenes. In this paper, the features extracted from the upper and lower channels are fused by the DCFFS (dual-channel feature fusion segmentation) module, and the semantic segmentation of the real scene is performed. The experimental results show that the mean intersection over union (MIOU) of the proposed DCFNet segmentation method on the S3DIS Area5 indoor scene dataset and the STPLS3D outdoor scene dataset reaches 71.18% and 48.87% respectively. The mean average precision (MACC) and overall accuracy (OACC) reach 77.01% and 86.91% respectively, achieving high-precision point cloud semantic segmentation in real scenes. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.