双目立体视觉是计算机视觉范畴的核心之一，它利用双目相机来获得目标物体的图像，经过物体图像处理 之后得到目标物体所在场景环境的三维信息，最终实现非接触条件下测距，简单便捷。本次毕业设计主要内容为研究 基于双目立体视觉平台上的图像匹配以及目标物体的距离测量技术，图像特征提取部分研究了 SIFT 算法和 SURF 算 法，特征匹配部分研究了 BF 法和 FLANN 法，距离测量研究主要通过视差深度的计算，结合视觉坐标系的转换实现 三维位置的定位与测量。结合维视双目立体视觉测量平台 MV-VS220，通过 Python+OpenCV 设计实现一个具备图像 采集、图像匹配以及距离测量等功能的原型演示系统。系统可实现对关键环节的过程与结果的演示，以及不同算法的 性能比较。系统测试表明，所开发的原型演示系统从界面、功能与性能方面均达到了设计的要求。

存放之前使用c++编写的双目立体视觉三维重建的相关代码和文件

Real-time binocular stereo vision FPGA system with OV5640 cameras

通过双目视觉测距

基于SIFT特征匹配的双目立体视觉测距

Generate disparity map with binocular camera and calculate distance

This project accomplishes the task of 3D reconstruction based on binocular stereo vision. It realizes the entire process from camera calibration, image matching, to point cloud visualization.

毕设项目，关键词：双目视觉 立体匹配 相机标定，使用OpenCV-Python完成

双目视觉的论文和总结

Real-time binocular stereo vision FPGA system with OV5640 cameras

Python基于OpenCV的双目视觉深度图算法（源码＆教程）

Grab pictures from stereo camera or a ZED camera and automatic calibrate the binocular vision system with a high accuracy.

With this program, you can control the robotic arm with your hands with the help of binocular vision and YOLOV5.....

基于opencv的双目视觉进行三维物体测量

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Because the traditional matching cost calculation algorithm is difficult to meet the accuracy requirements of complex tasks. In recent years, deep learning methods have made successful breakthroughs in image processing. Because of their superior performance in self-learning and feature information extraction, we aim to use deep learning algorithms to calculate high-precision initial matches cost.

Target location and tracking technology based on binocular vision

基于双目视觉的物体体积测量算法研究 （RESEARCH ON THE ALGORITHM OF OBJECT VOLUME MEASUREMENT ON BINOCULAR VISION）

Binocular vision 3D reconstruction

[arXiv '24] Real-Time 3D Semantic Scene Perception for Egocentric Robots with Binocular Vision

No description available

Code for "CNN^2: Viewpoint Generalization via a Binocular Vision" (NeurIPS 2019)

No description available

Binocular Stereo Vision: calibration, rectify, stereo matching using sad/sgbm/bm/gc

Restoring binocular vision

Scene depth estimation based on binocular stereo vision.

The project goal is to use binocular vision and non-contact measurement methods to identify modal parameters of large antennas, solar wings, etc. in orbit.

Performing Dense correspondence matching and Depth Image Generation using Patch Matching and Gradients-Based Features algorithms.

This is an project of moving object tracking based on binocular stereo vision.

No description available