2018 Computer Vision

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Academic unit or major
Graduate major in Systems and Control Engineering
Okutomi Masatoshi 
Class Format
Media-enhanced courses
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Syllabus updated
Lecture notes updated
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Course description and aims

The main topic of this course is how to reconstruct 3-D information in space from 2-D images. This course will introduce several cues for reconstruction. Then geometric modeling between space and images, and methods for reconstruction based on the modeling will be explained. In addition, photometric modeling of images and scene reconstruction methods will be introduced.

Student learning outcomes

1. Understanding the outline of 3-D reconstruction from 2-D images.
2. Understanding the mathematical model which represents the geometric relation between space and images.
3. Understanding the methods for "stereo vision" and "structure from motion".
4. Understanding the photometric model of images and the methods for scene reconstruction, such as object shape, surface reflectance, and illumination.


3-D reconstruction, geometric model, camera calibration, stereo vision, motion estimation, structure from motion, photometric model, reflectance characteristics

Competencies that will be developed

Specialist skills Intercultural skills Communication skills Critical thinking skills Practical and/or problem-solving skills

Class flow

The outline of 3-D reconstruction from images, its fundamental theories and methods will be explained.
The contents might be changed according to students' knowledge and interests.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Three Dimensional Reconstruction from Images Understanding the essential problem of 3-D Reconstruction from images and the relation with human vision.
Class 2 Geometric Relation between Space and Image Understanding geometric modeling to represent space-image relation and subsequent equations.
Class 3 Camera Calibration and Stereo Vision Understanding camera calibration methods. Understanding the principle and equations of stereo vision.
Class 4 Camera Motion Estimation and Structure from Motion Understanding camera motion estimation. Understanding the problem to estimate both camera motion and 3-D positions.
Class 5 Stereo Matching and Shape Recovery Understanding stereo maching methods, how to use muti-view cameras, and shape recovery.
Class 6 Photometric Analysis of Image Understanding how the pixel values are determined and its mathematical models.
Class 7 Reconstruction of Shape, Reflectance, and Illumination Understanding the methods for reconstructing the object shape, surface reflectance characterristics, and/or illumination distribution from images.


We will use the following reference book and handouts.

Reference books, course materials, etc.

Digital Image Processing: Computer Graphics Arts Society (CG-ARTS)

Assessment criteria and methods

The level of understanding about the contents presented in the course and the ability to apply them to problems will be asessed by submitted reports.

Related courses

  • SCE.I501 : Computaional Imaging

Prerequisites (i.e., required knowledge, skills, courses, etc.)

no prior conditions

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