Point Cloud Segmentation
Showing 7 of 7 projects
A curated list of papers, datasets, and code for 3D point cloud analysis research, covering classification, segmentation, detection, and more.
Fast and robust algorithm for segmenting Velodyne LiDAR point clouds into objects for autonomous driving applications.
A PyTorch framework for semantic segmentation of large 3D point clouds using superpoint graphs.
A C++ library for fast ground segmentation from LiDAR point clouds using the line-fit algorithm.
A ROS library for LiDAR point cloud segmentation, enabling ground removal and object clustering for autonomous vehicle perception.
A real-time ROS 2 package for detecting drivable roads and sidewalks from LIDAR point clouds in urban autonomous driving scenarios.
An algorithm for optimal worst-case instance segmentation of LiDAR point clouds using objectness scoring.
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