Vision Algorithm Platform — Virtual simulation based on point cloud algorithm

This software is an online algorithm container platform that can be used for intelligent welding, welding process optimization, and other production scenarios that use laser point clouds as data sources. It can perform point cloud processing online simulation in the platform, accumulate algorithms, combine experimental algorithms, and save the operating costs of robotic arms. 1. Technical roadmap The backend algorithm container uses C++language, while the web frontend mainly uses VUE and Three.js for 3D visualization. The real-time interaction between the frontend and backend uses WebSocket. The algorithm container supports various process structures such as sequence, branch, and loop, which can meet complex experimental needs. 2. Solution Welding process monitoring and tracking: Real time monitoring of welding process, precise tracking of weld position and shape. Timely detection of welding defects to improve welding quality and efficiency. Welding process optimization: Optimize welding process parameters through depth map analysis and parameter adjustment. Reduce waste rate and improve production efficiency. Intelligent welding solution: Applying machine vision and intelligent algorithms to the welding process to achieve automation and intelligent control. Reduce labor costs and improve production efficiency. 3. Performance Performance This platform directly reads the memory of the C++server and sends point cloud data for real-time transmission between the front-end and back-end. The front-end can render point cloud computing results in milliseconds, and the maximum number of point cloud renderings depends on the GPU hardware performance of the browser (using WebGL technology). With the Thinkpad T590 (low-voltage 8th generation I7, no dedicated graphics), it can smoothly render 6 million point clouds. 4. Design Idea This platform can serve as a container for various algorithms, with functions for algorithm management, expansion, and dynamic scheduling. It can flexibly expand various algorithms, freely combine multiple experimental scenarios, and provide efficient and real-time access to 3D point cloud data, helping users quickly build, reuse, and debug algorithm processes and parameters. If you are interested in this project, we can provide a 14 day free trial and free remote deployment and debugging.