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Topics in Computer Graphics

Topics in Computer Graphics

Code	14496
Year	1
Semester	S1
ECTS Credits	6
Workload	OT(15H)
Scientific area	Informatics
Entry requirements	N/A
Mode of delivery	- Face-to-face and brainstorming.
Work placements	N/A.
Learning outcomes	This course focuses on reviewing and understanding current computer graphics and visualization techniques and problems. Research topics are inspired by recent papers from the ACM SIGGRAPH and Eurographics/EuroVis conferences, through student-led discussions and oral and visual presentations, albeit teacher-coordinated. The general objectives of the curricular unit are the following: - Provide doctoral students with a broad view of computer graphics and its applications in science and engineering. - Provide doctoral students with strong skills in scientific research methodologies. With regard to specific learning objectives, at the end of the course, the doctoral student must be able to describe and implement at least one algorithm for: - global rendering of 3D scenes based on the image; - animation and molecular visualization based on physical equations of motion; - geometric segmentation using machine learning techniques.
Syllabus	01. Introduction: trends and challenges in computer graphics. 02. Global illumination techniques of dynamic scenes. 03. Computer animation techniques. 04. Perception and aesthetics of the movement. 05. Processing of dynamic deformations of triangulated surfaces and meshes. 06. Simulation and rendering of natural phenomena (e.g., liquids, clouds, etc.). 07. Simulation, rendering and visualization of molecular phenomena (p.exe., protein-ligand interactions). 08. Machine learning in computer graphics and visualization.
Main Bibliography	Principal/Main: - Graphics & Visualization, Principles and Algorithms, T. Theoharis, G. Papaioannou, N. Platis, N. Patrikalakis, AK Peters, 2008. - Real-time Rendering (4th ed.), T. Akenine-Moller, E. Haines, and N. Hoffman, AK Peters, 2018. - Physically Based Rendering: From Theory To Implementation (3rd ed.), M. Pharr and G. Humphreys, Morgan Kaufmann, 2016. Scientific Visualization: Uncertainty, Multifield, Biomedical, and Scalable Visualization, Charles D. Hansen, Min Chen, Christopher R. Johnson, Arie E. Kaufman, and Hans Hagen (eds.), Springer, 2014. - Data Visualization: Charts, Maps, and Interactive Graphics, R. Grant, CRC Press, 2018. Complementar/Complementary: - Implicit Curves and Surfaces: Mathematics, Data Structures, and Algorithms, A. Gomes, I. Voiculescu, J. Jorge, B. Wyvill, and C. Galbraith, Springer-Verlag, 2009. - Histochemical and Cytochemical Methods of Visualization (1st ed.), Jean-Marie Exbrayat (ed.), CRC Press, 2013.
Teaching Methodologies and Assessment Criteria	In order for the student to acquire the skills (see learning objectives) required in the curricular unit, the following are foreseen: - 1h/week of 1 OT class. Doctoral students are required to study the articles associated with each chapter before the class takes place. In addition, whenever a chapter is completed, each doctoral student has to deliver and demonstrate the operation of an algorithm/project assigned to him by the professor. - 1h/week of tutoring outside the normal classroom environment. This monitoring of doctoral students aims to promote the resolution of problems posed by individual projects. Evaluation: - 2 mini-projects (5 marks each) - 1 final project worth 10 marks.
Language	Portuguese. Tutorial support is available in English.