bzkrtslh/Enriched-PointNetPP-and-RandLA-NET-Point-Cloud-Semantic-Segmentation
This GitHub repository has been created for the research project titled "Improving Aerial Targeting Precision: A Study on Point Cloud Semantic Segmentation with Advanced Deep Learning Algorithms."
This project helps classify features within 3D point cloud data, specifically for aerial imagery. It takes raw 3D point cloud scans as input and outputs a segmented point cloud where different objects (like buildings, trees, or vehicles) are identified. This is useful for geomatics engineers, urban planners, or defense analysts who work with aerial reconnaissance and geospatial intelligence.
No commits in the last 6 months.
Use this if you need to automatically identify and categorize objects within large 3D point cloud datasets captured from aerial sources.
Not ideal if your primary goal is 2D image analysis, object detection in standard photographs, or if you need a solution for non-aerial 3D data.
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May 18, 2024
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