Degree | Type | Year | Semester |
---|---|---|---|
4318299 Computer Vision | OB | 0 | 1 |
You can check it through this link. To consult the language you will need to enter the CODE of the subject. Please note that this information is provisional until 30 November 2023.
Degree in Engineering, Maths, Physics or similar
Module Coordinator: Dr. Gloria Haro
The goal of this module is to learn the principles of the 3D reconstruction of an object or a scene from multiple images or stereoscopic videos. For that, the basic concepts of the projective geometry and the 3D space are firstly introduced. The rest of the theoretical aspects and applications are built upon these basic tools. The mapping from the 3D world to the image plane will be studied, for that we will introduce different camera models, their parameters and how to estimate them (camera calibration and auto-calibration). The geometry that relates a pair of views will be analyzed. All these concepts will be applied to obtain a 3D reconstruction in the two main possible settings: calibrated or uncalibrated cameras. In particular, we will learn how to: estimate the depth of image points, extract the underlying 3D points given a set of point correspondences in the images, generate novel views, estimate the 3D object given a set of calibrated color images or binary images, and estimate a sparse set of 3D points given a set of uncalibrated images. The 3D representation in voxels and meshes will be studied. We will explain the reconstruction and modeling from Kinect data, as a particular model of sensors that provide an image of the scene together with its depths. Finally, we will see some techniques for processing 3D point clouds. The concepts and techniques learnt in this module are used in real applications ranging from augmented reality, object scanning, motion capture, new view synthesis, bullet-time effect, robotics, etc.
Supervised sessions: (Some of these sessions could be synchronous on-line sessions)
Directed sessions:
Autonomous work:
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Title | Hours | ECTS | Learning Outcomes |
---|---|---|---|
Type: Directed | |||
Lecture sessions | 20 | 0.8 | KA04, KA12, KA04 |
Type: Supervised | |||
Project follow-up sessions | 8 | 0.32 | CA01, CA06, SA04, SA10, SA15, SA17, CA01 |
Type: Autonomous | |||
Homework | 113 | 4.52 | CA01, CA06, SA04, SA10, SA15, SA17, CA01 |
The final mark for this module will be computed with the following formula:
Final Mark = 0.4 x Exam + 0.55 x Project+ 0.05 x Attendance
where,
Exam: is the mark obtained in the Module Exam (must be >= 3).
Attendance: is the mark derived from the control of attendance at lectures (minimum 70%)
Projects: is the mark provided by the project coordinator based on the weekly follow-up of the project and deliverables (must be >= 5). All accordingly with specific criteria such as:
Only those students that fail (Final Mark < 5.0) can do a retake exam.
Title | Weighting | Hours | ECTS | Learning Outcomes |
---|---|---|---|---|
Exam | 0.4 | 2.5 | 0.1 | KA04, KA12 |
Project | 0.55 | 6 | 0.24 | CA01, CA06, SA04, SA10, SA15, SA17 |
Session attendance | 0.05 | 0.5 | 0.02 | CA06, KA04, KA12 |
Books:
Tutorials:
Python Programming Tools with special attention to computer vision and image processing libraries