Studying at the University of Verona
Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.
Academic calendar
The academic calendar shows the deadlines and scheduled events that are relevant to students, teaching and technical-administrative staff of the University. Public holidays and University closures are also indicated. The academic year normally begins on 1 October each year and ends on 30 September of the following year.
Course calendar
The Academic Calendar sets out the degree programme lecture and exam timetables, as well as the relevant university closure dates..
For the year 2019/2020 No calendar yet available
Exam calendar
Exam dates and rounds are managed by the relevant Science and Engineering Teaching and Student Services Unit.
To view all the exam sessions available, please use the Exam dashboard on ESSE3.
If you forgot your login details or have problems logging in, please contact the relevant IT HelpDesk, or check the login details recovery web page.
Should you have any doubts or questions, please check the Enrolment FAQs
Academic staff
Study Plan
The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University. Please select your Study Plan based on your enrolment year.
Training offer to be defined
Legend | Type of training activity (TTA)
TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.
Digital design (2020/2021)
Teaching code
4S009010
Teacher
Coordinatore
Credits
6
Language
English
Scientific Disciplinary Sector (SSD)
ING-INF/05 - INFORMATION PROCESSING SYSTEMS
Period
II semestre dal Mar 1, 2021 al Jun 11, 2021.
Learning outcomes
This course is aimed at providing the student with the practical and theoretical tools for virtual representation of objects and complex scene, and the analysis and modelling of three-dimensional data. Formal methods are provided for the digital processing of geometric structures, the 3D acquisition pipeline, and the analysis of rigid and non-rigid shapes. Particular emphasis will be devoted for 3D digital content creation from the observation of real scenes.
At the end of the course, this knowledge will allow the student to: implement a new system for the generation of virtual environments from the observation of real scenes, in a research context, combining formal methods for the analysis of rigid and non-rigid 3D shapes; implement different versions of 3D acquisition pipeline and analyse geometric properties of acquired shapes; deal with object deformation of non-rigid shapes.
In addition, the student must be able to identify the most appropriate 3D modelling method for a given applicative context, including the capability of extending the digital content design system to other disciplines like machine learning and robotics; the student will be independent in continuing the studies in 3D digital design from the observation of the real scenes.
The student will then be able to present the results of a digital design project, discuss with professional experts in the field, and provide the capability of adapting to the technical evolution and state of the art in the 3D digital content design.
Program
Introduction
3D data examples and representations
Differential geometry and 3D data
3D data acquisition
Registration and matching: ICP algorithm, pointwise descriptors
Analysis and computation on 3D data: normals, distances and operator
Spectral geometry processing
Mesh sampling and generation
AI and 3D data
3D Printing
Applications and conclusions
| Author | Title | Publishing house | Year | ISBN | Notes |
|---|---|---|---|---|---|
| Alexander M. Bronstein, Michael M. Bronstein, Ron Kimmel | Numerical Geometry of Non-Rigid Shapes (Edizione 1) | Springer-Verlag New York 2009 | 2009 | 978-0-387-73300-5 | |
| Mario Botsch, Leif Kobbelt, Mark Pauly, Pierre Alliez, Bruno Levy | Polygon mesh processing | A K Peters/CRC Press | 2010 | 9781568814261 | |
| Do Carmo | Riemannian Geometry | 1992 |
Examination Methods
The exam consists of an oral test composed of two parts:
1) Part chosen by the student (one of the three options A, B, and C), related to the topics presented during the course (to be agreed via e-mail with the teacher).
2) Questions on the content of the course or on the exercises to evaluate and finalize the final score of the exam.
Options for part 1) in an increasing value for the final evaluation (to be agreed via e-mail with the teacher):
A) Seminar on a specific part of the course (theory+exercises).
B) Seminar on a research paper.
C) Implementation of a small project (mandatory for 30 cum laude).
There is not any difference between attending and non-attending students.
Bibliography
Type D and Type F activities
Training offer to be defined
Career prospects
Module/Programme news
News for students
There you will find information, resources and services useful during your time at the University (Student’s exam record, your study plan on ESSE3, Distance Learning courses, university email account, office forms, administrative procedures, etc.). You can log into MyUnivr with your GIA login details.
Further services
I servizi e le attività di orientamento sono pensati per fornire alle future matricole gli strumenti e le informazioni che consentano loro di compiere una scelta consapevole del corso di studi universitario.
Graduation
List of theses and work experience proposals
| theses proposals | Research area |
|---|---|
| Domain Adaptation | Computer Science and Informatics: Informatics and information systems, computer science, scientific computing, intelligent systems - Computer graphics, computer vision, multi media, computer games |
| Domain Adaptation | Computer Science and Informatics: Informatics and information systems, computer science, scientific computing, intelligent systems - Machine learning, statistical data processing and applications using signal processing (e.g. speech, image, video) |
| Domain Adaptation | Computing Methodologies - IMAGE PROCESSING AND COMPUTER VISION |
| Domain Adaptation | Computing methodologies - Machine learning |
Attendance
As stated in point 25 of the Teaching Regulations for the A.Y. 2021/2022, attendance at the course of study is not mandatory.Please refer to the Crisis Unit's latest updates for the mode of teaching.
