Point Cloud Processing
Showing 33 of 33 projects
A standalone, large-scale open-source library for 2D/3D image and point cloud processing.
A computationally efficient and robust LiDAR-inertial odometry (LIO) package using a tightly-coupled iterated Kalman filter.
A curated list of papers, datasets, and code for 3D point cloud analysis research, covering classification, segmentation, detection, and more.
A lightweight, ground-optimized lidar odometry and mapping system for ROS-compatible unmanned ground vehicles.
A clean, simplified implementation of the LOAM algorithm for real-time LiDAR odometry and mapping using Eigen and Ceres Solver.
A ROS package for robot-centric elevation mapping that handles pose uncertainty for navigation on rough terrain.
A realtime LiDAR odometry and mapping (LOAM) method for state estimation and mapping using 3D lidar sensors like Velodyne VLP16.
A Python library for 3D point cloud processing that leverages the scientific Python stack for complex operations with minimal code.
A target-less, automatic toolbox for LiDAR-camera extrinsic calibration that works with various sensor models without requiring calibration targets.
A deep learning framework for feature learning directly from point clouds using X-Conv operations, achieving state-of-the-art results in classification and segmentation.
A C++ library for translating and manipulating point cloud data, analogous to GDAL for raster/vector data.
A lean and fast C++ library for 3D point cloud data processing with efficient implementations of common operations.
A tightly coupled 3D LiDAR-inertial odometry and mapping system for real-time robot localization and mapping.
A C++ ROS package for real-time detection, tracking, and classification of static and dynamic objects from LIDAR point clouds.
Generates an octree LOD structure for streaming and real-time rendering of massive point clouds in web browsers and desktop applications.
Detects 6-DOF grasp poses for parallel jaw grippers in 3D point clouds, enabling robotic grasping of novel objects in clutter.
A CUDA-accelerated library collection for point cloud processing, providing GPU-optimized alternatives to PCL functions.
A learning-based approach for moving object segmentation in 3D LiDAR data, distinguishing moving vs. static objects in real-time.
A LiDAR-based tool for constructing static maps by removing dynamic points from point cloud sequences.
A Python package for visualizing and processing 2D/3D point clouds with interactive rendering and parallelized queries.
A convolutional neural network model for real-time road-object segmentation from 3D LiDAR point clouds.
A simple, robust, and accurate 3D LiDAR SLAM system designed to just work.
A Python implementation for fully automatic extrinsic calibration of 3D LiDAR and cameras using laser reflectance intensity.
A C++/TensorRT inference module for RangeNet++, enabling fast LiDAR semantic segmentation for robotics applications.
A simple, multi-language implementation of the Iterative Closest Point algorithm for 3D point cloud registration.
A full LiDAR SLAM system for static environment mapping using LiDAR with optional GPS, IMU, and odometry support.
A ROS stack providing perception packages for 2D image and 3D point cloud processing in robotics.
ROS package for sensor processing, object detection, tracking, and evaluation using the KITTI Vision Benchmark dataset.
A self-supervised deep learning model for extrinsic calibration between LiDAR and camera sensors using 3D spatial transformer networks.
Automatically classifies and labels urban point clouds using data fusion with public datasets and region growing techniques.
C++ libraries for multi primitive-to-primitive ICP algorithms and flexible point cloud processing pipelines.
A ROS library for robust plane segmentation from LIDAR, depth camera data, and elevation maps using normal-based clustering.
A high-precision, grid-based C++ library for ground segmentation in LiDAR point clouds, designed for safety-critical autonomous driving and robotics.
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