WiMi Hologram Cloud announces image-fused point cloud semantic segmentation

WiMi Hologram Cloud announced an image-fused point cloud semantic segmentation method based on fused graph convolutional network, aiming to utilize the different information of image and point cloud to improve the accuracy and efficiency of semantic segmentation. Point cloud data is very effective in representing the geometry and structure of objects, while image data contains rich color and texture information. Fusing these two types of data can utilize their advantages simultaneously and provide more comprehensive information for semantic segmentation. The fused graph convolutional network is an effective deep learning model that can process both image and point cloud data simultaneously and efficiently deal with image features of different resolutions and scales for efficient feature extraction and image segmentation. FGCN is able to utilize multi-modal data more efficiently by extracting the semantic information of each point involved in the bimodal data of the image and point cloud. To improve the efficiency of image feature extraction, WiMi also introduces a two-channel k-nearest neighbor module. This module allows the FGCN to utilize the spatial information in the image data to better understand the contextual information in the image by computing the semantic information of the k nearest neighbors around each point. This helps FGCN to better distinguish between more important features and remove irrelevant noise. In addition, FGCN employs a spatial attention mechanism to better focus on the more important features in the point cloud data. This mechanism allows the model to assign different weights to each point based on its geometry and the relationship of neighboring points to better understand the semantic information of the point cloud data. By fusing multi-scale features, FGCN enhances the generalization ability of the network and improves the accuracy of semantic segmentation. Multi-scale feature extraction allows the model to consider information in different spatial scales, leading to a more comprehensive understanding of the semantic content of images and point cloud data.