Wimi Introduces Image-Fused Point Cloud Semantic Segmentation With Fusion Graph Convolutional Network

WiMi Hologram Cloud a leading global Hologram Augmented Reality (“AR”) Technology provider, 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.

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The fused graph convolutional network (FGCN) 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 (KNN) 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.

This image-fused point cloud semantic segmentation with fusion graph convolutional network is able to utilize the information of multi-modal data such as images and point clouds more efficiently to improve the accuracy and efficiency of semantic segmentation, which is expected to advance machine vision, artificial intelligence, photogrammetry, remote sensing, and other fields, providing new a method for future semantic segmentation research.

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This image-fused point cloud semantic segmentation with fusion graph convolutional network has a wide range of application prospects and can be applied in many fields such as autonomous driving, robotics, and medical image analysis. With the rapid development of autonomous driving, robotics, medical image analysis and other fields, there is an increasing demand for processing and semantic segmentation of image and point cloud data. For example, in the field of autonomous driving, self-driving cars need to accurately perceive and understand the surrounding environment, including semantic segmentation of objects such as roads, vehicles, and pedestrians. This image-fused point cloud semantic segmentation with fusion graph convolutional network can improve the perception and understanding of the surrounding environment and provide more accurate data support for decision making and control of self-driving cars. In the field of robotics, robots need to perceive and understand the external environment in order to accomplish various tasks. Image fusion point cloud semantic segmentation with fusion graph convolutional network can fuse image and point cloud data acquired by robots to improve the ability to perceive and understand the external environment, which helps robots to better accomplish tasks. In the medical field, medical image analysis requires accurate segmentation and recognition of medical images to better assist medical diagnosis and treatment. The image-fused point cloud semantic segmentation with fusion graph convolutional network can fuse medical images and point cloud data to improve the segmentation and recognition accuracy of medical images, thus providing more accurate data support for medical diagnosis and treatment.

In the future, WiMi research will further optimize the model structure. At the same time, the model will be combined with deep learning technology to take advantage of deep learning technology to improve the performance of the model. And further develop the multi-modal data fusion technology to fuse different types of data (e.g., image, point cloud, text, etc.) to provide more comprehensive and richer information and improve the accuracy of semantic segmentation. WiMi will continue to improve the real-time processing of the image-fused point cloud semantic segmentation with fusion graph convolutional network capability to meet the demand.

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