🧠🕸️ Graph-SENet: Unsupervised Skeleton Extraction from Point Clouds
🔍 Introduction
With the rapid growth of 3D sensing technologies such as LiDAR, RGB-D cameras, and laser scanners, point cloud data has become central to computer vision and geometric learning 📊. One of the key challenges is skeleton extraction—deriving a compact structural representation of complex 3D shapes 🦴.
🚀 What is Graph-SENet?
Graph-SENet is an unsupervised learning–based Graph Neural Network (GNN) designed to extract skeletal structures directly from raw point clouds 🧩. Unlike supervised models, it does not require labeled skeleton data, making it scalable and cost-effective 🔄.
🧠 How It Works
Graph-SENet models point clouds as graphs 🕸️, where nodes represent points and edges capture local geometric relationships. Through graph convolution and structure-aware learning, the network identifies medial axes and topological connections while preserving shape integrity 📐.
🌟 Key Features
-
🔓 Unsupervised Learning – No ground-truth skeletons required
-
🧬 Topology Preservation – Maintains object structure and connectivity
-
⚡ Efficient Representation – Reduces complex shapes to meaningful skeletons
-
🔄 Robust to Noise – Handles sparse and irregular point clouds
🧪 Applications
Graph-SENet has wide-ranging applications across domains:
-
🤖 Robotics & Motion Planning
-
🏥 Medical Imaging (vascular and organ modeling)
-
🏗️ 3D Reconstruction & CAD
-
🌍 Autonomous Driving & Scene Understanding
🏁 Conclusion
By combining graph neural networks with unsupervised learning, Graph-SENet offers a powerful and flexible approach to skeleton extraction from point clouds 🌐. It opens new pathways for efficient 3D shape analysis without the burden of labeled datasets ✨.
Scientific World Research Awards🏆
Visit our page : https://scientificworld.net/
Nominations page📃 : https://scientificworld.net/award-nomination/?ecategory=Awards&rcategory=Awardee
Get Connects Here:
==================
Youtube: https://www.youtube.com/@Scientificresearch-04
Instagram : https://www.instagram.com/swr_awards/
Blogger :https://www.blogger.com/blog/posts/8295489504259175195?hl=en&tab=jj
Twitter :https://x.com/SWR_Awards
What'sApp: https://whatsapp.com/channel/0029Vb5WOsUH5JLpZ1w0RD2M 3
No comments:
Post a Comment