• Feb. 26. Handout, slides, and recording of lecture 4 are available. Checkout here.

  • Feb. 19. Handout, slides, and recording of lecture 3 are available. Checkout here.

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About

Photogrammetry and 3D Computer Vision (i.e., 3DV) aim at recovering the structure of real-world objects/scenes from visual data (i.e., images, point clouds). This course is about the theories, methodologies, and techniques of 3D computer vision for the built environment. In the term of this course, you will learn the basic knowledge and algorithms in 3D computer vision through a series of lectures, reading materials, and lab exercises.

Contents

The topics of this course cover the whole pipeline of reconstructing 3D models from images, as well as semantic segmentation/classification of point clouds:

Goals

After finishing this course, you will have built a working knowledge of the theory, methodology, and algorithms/techniques used in 3D computer vision. Specifically, you will be able to:

Assessment

The assessment of this course consists of 3 group assignments and the final exam. The final grade will be based on the contribution in doing the assignments and the final exam, breaking down to:

Both assignments and the final exam have to be graded sufficiently (5.5 minimum) to pass the course. A total of 6.0 or above is necessary to successfully pass the course.

Communication

Based on past experiences of teaching staffs and students preferences, we will mainly use the following communication tools for our course:

In addition to the regular course hours, it is also possible to meet the teachers individually (by appointment only). When you make an appointment, please indicate two time slots and a link to the meeting room (could be Zoom, Teams, whatever).

Lecturers and teaching assistants

Liangliang Liangliang Liangliang
Liangliang Nan Nail Ibrahimli Shenglan Du
LiangliangNan#0976 nibrahimli#5857 Shenglan Du#2136

Copyright @Liangliang Nan. 2021