У нас вы можете посмотреть бесплатно Stop Using Pinhole Models: Next-Gen Camera Calibration with StereoComplex или скачать в максимальном доступном качестве, видео которое было загружено на ютуб. Для загрузки выберите вариант из формы ниже:
Если кнопки скачивания не
загрузились
НАЖМИТЕ ЗДЕСЬ или обновите страницу
Если возникают проблемы со скачиванием видео, пожалуйста напишите в поддержку по адресу внизу
страницы.
Спасибо за использование сервиса ClipSaver.ru
Is your 3D computer vision project suffering from the "Calibration Plateau"? In this explainer, we dive into StereoComplex, a research prototype that challenges the traditional pinhole camera model to solve one of the biggest headaches in robotics and AR: accurate camera calibration. Most 3D projects fail because of poor input data. StereoComplex introduces a novel 2-step approach: fixing 2D feature detection before attempting 3D reconstruction, and using a flexible Ray-Based model (Zernike polynomials) instead of the rigid pinhole assumption. Key Takeaways: Why standard OpenCV calibration struggles with image noise and compression. How 2D Ray-Field Correction reduces corner detection error by over 50%. The shift from Pinhole models to 3D Ray-Based models for higher stability. How to get state-of-the-art results with a simple planar target (no complex robotics lab needed). This tool is a lightweight Python package designed to slot between basic OpenCV scripts and heavy robotics frameworks like ROS. Find this work on the jeffwitz github account