Hands-On Computer Vision: SLAM, 3d geometry, Calib, AR, Pose
Hands-On Computer Vision: SLAM, 3d geometry, Calib, AR, Pose
Published 7/2025
Duration: 2h 27m | .MP4 1280x720 30 fps(r) | AAC, 44100 Hz, 2ch | 1.13 GB
Genre: eLearning | Language: English
Published 7/2025
Duration: 2h 27m | .MP4 1280x720 30 fps(r) | AAC, 44100 Hz, 2ch | 1.13 GB
Genre: eLearning | Language: English
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 to3D computer visionusing monocular and stereo cameras. Through a series of real-world projects and coding exercises, learners will build a strong foundation incamera geometry,feature-based matching,pose estimation, and3D reconstruction targeted for research and industrial application in Autonomous vehicle, robotics, machine learning, 3d geometry and reconstruction.
You will begin by understandingcamera calibrationand how a single camera can be used forlocalization and height estimation. You'll then move on to more advanced topics likereal-time 3D pose estimation,augmented reality overlays,video stabilization, andvisual odometryon real datasets like KITTI.
This course is project-driven and emphasizes classical, interpretable 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 ofcamera models and calibration
Hands-on experience withOpenCVfor vision pipelines
Real-time3D pose estimationand augmented reality overlay
Proficiency inhomography estimationandimage registration
Building basicvisual odometrysystems from scratch
Creating and visualizing3D reconstructionsusing triangulation
Working with real datasets likeKITTIfor 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
More Info