Lidar Processing
Showing 19 of 19 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 C++ library for fast ground segmentation from LiDAR point clouds using the line-fit algorithm.
A PyTorch framework for semantic segmentation of large 3D point clouds using superpoint graphs.
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.
An algorithm for optimal worst-case instance segmentation of LiDAR point clouds using objectness scoring.
A framework for developing and evaluating LiDAR data processing algorithms, with support for hardware acceleration on embedded platforms.
Python implementation of scan unfolding for KITTI LiDAR data to create dense cylindrical projections without systematic discretization artifacts.
Related Tags
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.