Hands-On Computer Vision: SLAM, 3d geometry, Calib, AR, Pose
Practical Computer Vision: 3D Geometry, Pose Estimation, and Augmented Reality
What you'll learn
3D Reconstruction via Stereo Triangulation from Two Views
Monocular Visual Odometry Using Epipolar Geometry and Optical Flow on KITTI Dataset
Real-Time 3D Pose Estimation and Augmented Reality Box Overlay from Video Using Feature Matching (Face Recognition)
Epipolar Geometry Visualization Using Fundamental Matrix
2D Video Stabilization Using Feature Tracking and Homography
Planar Image Stitching Using BRISK Feature Matching and Homography
Object Localization and Height Estimation Using Monocular Camera Calibration and Grid Projection
Requirements
python
Description
This hands-on course introduces students to 3D computer vision using monocular and stereo cameras. Through a series of real-world projects and coding exercises, learners will build a strong foundation in camera geometry, feature-based matching, pose estimation, and 3D reconstruction targeted for research and industrial application in Autonomous vehicle, robotics, machine learning, 3d geometry and reconstruction.You will begin by understanding camera calibration and how a single camera can be used for localization and height estimation. You'll then move on to more advanced topics like real-time 3D pose estimation, augmented reality overlays, video stabilization, and visual odometry on real datasets like KITTI.This course is project-driven and emphasizes classical, interpre table methods giving you the tools to develop your own computer vision pipeline without requiring deep learning.
What You Will Learn:
Camera Calibration & Projection Geometry Estimate intrinsic and extrinsic parameters of monocular cameras Use projection grids for object height estimation Object Localization & 3D Pose Estimation Detect and track objects using feature matching Estimate 3D object pose and overlay augmented content in real-time Video Stabilization & Image Stitching Implement 2D video stabilization using feature tracking and homographies Perform planar image stitching using BRISK and homography transformation Feature Detection and Matching Use BRISK, ORB, and other descriptors for robust keypoint matching Understand outlier rejection using RANSAC Epipolar Geometry & Visual Odometry Compute and visualize the fundamental matrix and epipolar lines Apply monocular visual odometry using optical flow and epipolar constraints 3D Triangulation from Stereo Views Reconstruct 3D point clouds from stereo image pairs Understand triangulation using projection matrices Skills You Will Gain:Practical understanding of camera models and calibration Hands-on experience with Open CV for vision pipelines Real-time 3D pose estimation and augmented reality overlay Proficiency in homography estimation and image registration Building basic visual odometry systems from scratch Creating and visualizing 3D reconstructions using triangulation Working with real datasets like KITTI for visual SLAM foundations Ideal For:Engineering and CS students Robotics and AR/VR enthusiasts Developers interested in classical computer vision techniques Anyone seeking a practical foundation before diving into deep learning
Who this course is for
All level python developers
Published 7/2025
Created by Ezeuko Emmanuel
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 11 Lectures ( 2h 26m ) | Size: 915 MB
Download
http://s9.alxa.net/one/2025/07/Hands...ib.AR.Pose.rar
Practical Computer Vision: 3D Geometry, Pose Estimation, and Augmented Reality
What you'll learn
3D Reconstruction via Stereo Triangulation from Two Views
Monocular Visual Odometry Using Epipolar Geometry and Optical Flow on KITTI Dataset
Real-Time 3D Pose Estimation and Augmented Reality Box Overlay from Video Using Feature Matching (Face Recognition)
Epipolar Geometry Visualization Using Fundamental Matrix
2D Video Stabilization Using Feature Tracking and Homography
Planar Image Stitching Using BRISK Feature Matching and Homography
Object Localization and Height Estimation Using Monocular Camera Calibration and Grid Projection
Requirements
python
Description
This hands-on course introduces students to 3D computer vision using monocular and stereo cameras. Through a series of real-world projects and coding exercises, learners will build a strong foundation in camera geometry, feature-based matching, pose estimation, and 3D reconstruction targeted for research and industrial application in Autonomous vehicle, robotics, machine learning, 3d geometry and reconstruction.You will begin by understanding camera calibration and how a single camera can be used for localization and height estimation. You'll then move on to more advanced topics like real-time 3D pose estimation, augmented reality overlays, video stabilization, and visual odometry on real datasets like KITTI.This course is project-driven and emphasizes classical, interpre table methods giving you the tools to develop your own computer vision pipeline without requiring deep learning.
What You Will Learn:
Camera Calibration & Projection Geometry Estimate intrinsic and extrinsic parameters of monocular cameras Use projection grids for object height estimation Object Localization & 3D Pose Estimation Detect and track objects using feature matching Estimate 3D object pose and overlay augmented content in real-time Video Stabilization & Image Stitching Implement 2D video stabilization using feature tracking and homographies Perform planar image stitching using BRISK and homography transformation Feature Detection and Matching Use BRISK, ORB, and other descriptors for robust keypoint matching Understand outlier rejection using RANSAC Epipolar Geometry & Visual Odometry Compute and visualize the fundamental matrix and epipolar lines Apply monocular visual odometry using optical flow and epipolar constraints 3D Triangulation from Stereo Views Reconstruct 3D point clouds from stereo image pairs Understand triangulation using projection matrices Skills You Will Gain:Practical understanding of camera models and calibration Hands-on experience with Open CV for vision pipelines Real-time 3D pose estimation and augmented reality overlay Proficiency in homography estimation and image registration Building basic visual odometry systems from scratch Creating and visualizing 3D reconstructions using triangulation Working with real datasets like KITTI for visual SLAM foundations Ideal For:Engineering and CS students Robotics and AR/VR enthusiasts Developers interested in classical computer vision techniques Anyone seeking a practical foundation before diving into deep learning
Who this course is for
All level python developers
Published 7/2025
Created by Ezeuko Emmanuel
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 11 Lectures ( 2h 26m ) | Size: 915 MB
Download
http://s9.alxa.net/one/2025/07/Hands...ib.AR.Pose.rar