ika-rwth-aachen/PCLSegmentation
Tensorflow 2.9 Pipeline for Semantic Point Cloud Segmentation with SqueezeSeqV2, Darknet21 and Darknet53.
This project helps self-driving car engineers and robotics researchers automatically identify and categorize objects within 3D LiDAR point cloud scans. It takes raw 3D point cloud data (X, Y, Z coordinates, intensity, depth, and optionally label IDs) and outputs segmented point clouds where different objects (like cars, pedestrians, or road signs) are distinguished. This allows for faster processing and analysis of sensor data, crucial for perception systems in autonomous vehicles.
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Use this if you need to precisely identify and classify objects in real-time from LiDAR sensor data for autonomous navigation or environmental mapping.
Not ideal if your primary need is 2D image segmentation or if you are working with non-LiDAR 3D data like photogrammetry meshes.
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24
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12
Language
Python
License
MIT
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Last pushed
Sep 05, 2022
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