This course challenges the misconception that computer vision is merely about importing pre-made models from repositories like GitHub or Hugging Face. It emphasizes the importance of understanding the foundational principles of computer vision and spatial perception to create genuine perception systems, rather than relying on “black box” solutions.
Participants will explore how images relate to three-dimensional reality through spatial geometry, learning about camera parameters (intrinsics and extrinsics) and how projection models convert images into spatial data. Key computer vision methods for robotics, including optical flow, feature matching, triangulation, and stereo vision, will be covered in detail.
The course will focus on developing a comprehensive 3D perception pipeline, encompassing camera calibration, distortion correction, depth mapping, and point cloud creation. Students will gain skills to transform pixel data into spatial knowledge, enabling them to design robust systems capable of functioning in real-world environments characterized by noise, calibration errors, and other challenges.
Targeted at engineers eager to move beyond ready-made libraries, this course aims to impart a deep understanding of the mathematics behind computer vision. It is ideal for those looking to design advanced perception systems for robotics, autonomous devices, and intelligent machines. By mastering the principles of camera geometry and spatial analysis, participants will be equipped to architect systems that truly comprehend their surroundings.