3D Reconstruction - Multiple Viewpoints
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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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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FullTime access
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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
Pioneer in Computational Imaging and Professor at Columbia University
Dr. Shree K. Nayar is the T. C. Chang Professor of Computer Science at Columbia University, where he leads the Columbia Vision Laboratory (CAVE). His research focuses on computational imaging and computer vision, with key interests in developing novel camera systems, physics-based models for vision and graphics, and algorithms for scene understanding from images. Dr. Nayar's work has significant applications across various fields, including robotics, virtual reality, augmented reality, and human-computer interfaces.He holds a B.E. in Electrical Engineering from the Birla Institute of Technology, an M.S. in Electrical and Computer Engineering from North Carolina State University, and a Ph.D. from Carnegie Mellon University. Throughout his career, Dr. Nayar has received numerous accolades for his contributions to the field, including the 2010 ACM Software Systems Award for his work on the GroupLens Recommender System. He has published over 300 scientific papers and holds more than 80 patents related to imaging technologies.Dr. Nayar teaches several courses on Coursera, including "3D Reconstruction - Multiple Viewpoints" and "Camera and Imaging," aimed at providing students with foundational knowledge in computer vision and imaging systems.
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