This course focuses on the recovery of the 3D structure of a scene from its 2D images. In particular, we are interested in the 3D reconstruction of a rigid scene from images taken by a stationary camera (same viewpoint). This problem is interesting as we want the multiple images of the scene to capture complementary information despite the fact that the scene is rigid and the camera is fixed. To this end, we explore several ways of capturing images where each image provides additional information about the scene.
This course is part of the First Principles of Computer Vision Specialization
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About this Course
Students should know the fundamentals of linear algebra and calculus. The knowledge of any programming language is beneficial, though not required.
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Try Coursera for BusinessWhat you will learn
Learn radiometric concepts related to light and how it interacts with scenes.
Understand reflectance models and the different physical mechanisms that determine the appearance of a surface.
Develop a method for recovering the shape of a surface from its shading.
Understand the principle of photometric stereo where a dense surface normal map of the scene is obtained by varying the illumination direction.
Skills you will gain
- Photometric Stereo
- Depth from Focus and Defocus
- Structed Light Methods
- Reflectance Models
- Radiometry
Students should know the fundamentals of linear algebra and calculus. The knowledge of any programming language is beneficial, though not required.
Could your company benefit from training employees on in-demand skills?
Try Coursera for BusinessOffered by
Syllabus - What you will learn from this course
Getting Started: 3D Reconstruction - Single Viewpoint
Radiometry and Reflectance
Photometric Stereo
Shape from Shading
Reviews
- 5 stars86.66%
- 4 stars13.33%
TOP REVIEWS FROM 3D RECONSTRUCTION - SINGLE VIEWPOINT
Amazing course , Well explained and interesting assignments!!!
Professor Nayar is an amazing teacher and the lectures are pure gold. FPCV is a great introduction to computer vision.
Excellent theoretical course, great content and the teacher explains very well. This course would be great with a complement of labs or small code practices.
About the First Principles of Computer Vision Specialization

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