Lidar Processing
Showing 16 of 16 projects
An open-source PyTorch toolbox for general 3D object detection, supporting LiDAR, camera, and multi-modal models.
A curated list of papers, datasets, and code for 3D point cloud analysis research, covering classification, segmentation, detection, and more.
A real-time baseline 3D multi-object tracking system using LiDAR point clouds, combining 3D Kalman filter and Hungarian algorithm.
A modular C++ library implementing the Iterative Closest Point (ICP) algorithm for aligning 2D and 3D point clouds in robotics and computer vision.
A collection of high-performance GICP-based point cloud registration algorithms with multi-threaded and GPU-accelerated implementations.
Fast and robust algorithm for segmenting Velodyne LiDAR point clouds into objects for autonomous driving applications.
Award-winning, efficient C++ tools for processing LiDAR data in LAS/LAZ formats with multi-core batch processing.
A multi-threaded, SSE-optimized Normal Distributions Transform algorithm for point cloud registration, offering up to 10x speedup over the original PCL implementation.
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 CUDA-accelerated library collection for point cloud processing, providing GPU-optimized alternatives to PCL functions.
An airborne LiDAR point cloud ground filtering method based on cloth simulation for bare earth extraction.
A real-time ROS 2 package for detecting drivable roads and sidewalks from LIDAR point clouds in urban autonomous driving scenarios.
Real-time Bayesian terrain traversability mapping and motion planning system for ROS-compatible unmanned ground vehicles using LiDAR point clouds.
A tool to convert aerial LIDAR pointcloud data into solid, watertight meshes suitable for 3D printing.
A ROS catkin package for correcting motion distortion in LiDAR scans using external 6DoF pose estimation.
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