This GitHub repository has been created for the research project titled "Improving Aerial Targeting Precision: A Study on Point Cloud Semantic Segmentation with Advanced Deep Learning Algorithms."

A command line toolkit to generate maps, point clouds, 3D models and DEMs from drone, balloon or kite images. 📷

Point Cloud Library (PCL)

The public CGAL repository, see the README below

Mesh processing library

💫 [CVPR 2024] LiDAR4D: Dynamic Neural Fields for Novel Space-time View LiDAR Synthesis

An elegant Python interface for visualization on the web platform to interactively generate insights into multidimensional images, point sets, and geometry.

Pointcept: a codebase for point cloud perception research. Latest works: PTv3 (CVPR'24 Oral), PPT (CVPR'24), OA-CNNs (CVPR'24), MSC (CVPR'23)

A BVH implementation to speed up raycasting and enable spatial queries against three.js meshes.

A collection of GICP-based fast point cloud registration algorithms

A hackable lidar viewer

OpenPCDet Toolbox for LiDAR-based 3D Object Detection.

A 3D and 2D processing base on glam

Official ROS drivers for Ouster sensors (OS0, OS1, OS2, OSDome)

Lidar Obstacle Detection using RANSAC, PCA, and KD-tree Cluster

Draco is a library for compressing and decompressing 3D geometric meshes and point clouds. It is intended to improve the storage and transmission of 3D graphics.

platform for annotation of images, training an object detection model (fixed model, different datasets), running detection with pre-trained and in-app trained models, reviewing detection result on a georeferenced map. specialized for waste detection. management of legal landfills through input waste form and 3D point cloud scans.

User-friendly, commercial-grade software for processing aerial imagery. 🛩

Robust characterization of inside and outside in periodic spaces.