haibo-qiu/GFNet
[TMLR 2022] Geometric Flow Network for 3D Point Cloud Semantic Segmentation
GFNet helps autonomous vehicle engineers and robotics developers accurately identify and categorize objects within 3D point cloud data captured by LiDAR. It takes raw 3D point clouds, like those from SemanticKITTI or nuScenes datasets, and outputs a segmented point cloud where each point is labeled with its object class (e.g., car, pedestrian, road). This tool is designed for professionals building and refining perception systems in self-driving cars or advanced robotics.
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Use this if you need highly accurate semantic segmentation of 3D point clouds, particularly from automotive LiDAR sensors, to improve object detection and scene understanding for autonomous systems.
Not ideal if you are working with 2D image data or if your primary need is object detection bounding boxes rather than fine-grained point-level classification.
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42
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Language
Python
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Last pushed
Jan 10, 2023
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