A cross platform app for quick and easy still image camera matching

ROS package to find a rigid-body transformation between a LiDAR and a camera for "LiDAR-Camera Calibration using 3D-3D Point correspondences"

An Invitation to 3D Vision: A Tutorial for Everyone

Official fSpy importer for Blender

Fast computer vision library for SFM, calibration, fiducials, tracking, image processing, and more.

[INACTIVE LEGACY VERSION, FIND THE PROJECT HERE: https://github.com/gyroflow/gyroflow] Video stabilization using IMU motion data from internal or external logs

📷 📷 Stereo camera calibration using OpenCV and C++

📷 RAW image processing for Python, a wrapper for libraw

Easy to use and accurate hand eye calibration which has been working reliably for years (2016-present) with kinect, kinectv2, rgbd cameras, optical trackers, and several robots including the ur5 and kuka iiwa.

Advanced lane detection using computer vision

Light-weight camera LiDAR calibration package for ROS using OpenCV and PCL (PnP + LM optimization)

📖 This guide is to help you understand the basics of the computerized image and develop computer vision projects with OpenCV. Includes Python, Java, JavaScript, C# and C++ examples.

A generic and robust calibration toolbox for multi-camera systems

This is a package for extrinsic calibration between a 3D LiDAR and a camera, described in paper: Improvements to Target-Based 3D LiDAR to Camera Calibration. This package is used for Cassie Blue's 3D LiDAR semantic mapping and automation.

Augmented reality for Unreal Engine 4

An OpenCV-based structured light processing toolkit.

MicaSense RedEdge and Altum image processing tutorials

Code for ICCV2021 paper SPEC: Seeing People in the Wild with an Estimated Camera

[AAAI 2024] UCMCTrack: Multi-Object Tracking with Uniform Camera Motion Compensation. UCMCTrack achieves SOTA on MOT17 using estimated camera parameters.

Virtual camera is created only using opencv and numpy. It simulates a camera where we can control all its parameters, intrinsic and extrinsic to get a better understanding how each component in the camera projection matrix affects the final image of the object captured by the camera.