Master 3D scene reconstruction techniques using multi-view geometry and camera calibration for accurate digital model creation.
Master 3D scene reconstruction techniques using multi-view geometry and camera calibration for accurate digital model creation.
This comprehensive course explores methods for recovering 3D structure from multiple viewpoints in computer vision. Students learn camera calibration, stereo vision systems, structure from motion, and optical flow estimation. The course covers both theoretical foundations and practical applications in robotics, virtual reality, and autonomous navigation.
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English
What you'll learn
Master camera calibration techniques
Develop stereo vision systems
Implement structure from motion algorithms
Create optical flow estimations
Understand epipolar geometry
Build 3D scene reconstruction systems
Skills you'll gain
This course includes:
4.2 Hours PreRecorded video
25 assignments
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There are 5 modules in this course
This advanced course provides comprehensive coverage of 3D reconstruction techniques using multiple viewpoints. Students learn the complete pipeline from camera modeling and calibration to complex scene reconstruction. The curriculum covers stereo vision, uncalibrated reconstruction, optical flow estimation, and structure from motion algorithms.
Getting Started: 3D Reconstruction - Multiple Viewpoints
Module 1 · 2 Hours to complete
Camera Calibration
Module 2 · 16 Hours to complete
Uncalibrated Stereo
Module 3 · 20 Hours to complete
Optical Flow
Module 4 · 16 Hours to complete
Structure from Motion
Module 5 · 17 Hours to complete
Fee Structure
Instructor
T. C. Chang Professor of Computer Science
Shree K. Nayar is the T. C. Chang Professor of Computer Science at Columbia University, where he leads the Columbia Vision Laboratory (CAVE). His laboratory specializes in developing cutting-edge computational imaging and computer vision systems. Nayar’s research focuses on three primary areas: the creation of innovative cameras that offer new types of visual information, the design of physics-based models for vision and graphics, and the development of algorithms aimed at understanding and interpreting scenes from images.Professor Nayar’s work is highly interdisciplinary, bridging the domains of imaging, computer vision, robotics, virtual and augmented reality, visual communication, computer graphics, and human-computer interaction. His pioneering research has significant real-world applications in these fields, advancing both the technology and our understanding of visual systems.In addition to his research, Nayar is an educator, teaching several advanced courses at Columbia University, including 3D Reconstruction - Multiple Viewpoints, 3D Reconstruction - Single Viewpoint, Camera and Imaging, Features and Boundaries, and Visual Perception. These courses reflect his expertise in computer vision and imaging technologies and contribute to shaping the next generation of researchers and engineers in these fields.
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