l1997i/Rapid_Seg
🔥(ECCV 2024 Oral) RAPiD-Seg: Range-Aware Pointwise Distance Distribution Networks for 3D LiDAR Segmentation
RAPiD-Seg helps autonomous vehicles and robotics understand their surroundings by processing 3D LiDAR data. It takes raw LiDAR point clouds, which are just collections of points, and extracts meaningful features that describe the shapes and surfaces of objects. The output is a refined representation of the environment, which self-driving car engineers and robotics researchers can use for tasks like object detection and scene understanding.
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Use this if you need to extract robust and accurate features from 3D LiDAR point clouds, especially in varying environments where object ranges differ significantly.
Not ideal if your application does not involve 3D LiDAR data or requires processing other types of sensor input like standard 2D images.
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Python
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MIT
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Last pushed
Sep 02, 2025
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