Teaching plan for the course unit

 

 

Close imatge de maquetació

 

Print

 

General information

 

Course unit name: Machine Learning in Computer Graphics

Course unit code: 575046

Academic year: 2021-2022

Coordinator: Ricardo Jorge Rodrigues Sepúlveda Marques

Department: Faculty of Mathematics and Computer Science

Credits: 3

Single program: S

More information enllaç

 

 

Estimated learning time

Total number of hours 75

 

Face-to-face and/or online activities

32

(Due to the Covid-19 restrictions, we expect to have 50%-75% of in-person activities)

Independent learning

43

 

 

Teaching blocks

 

1. Block 1: Introduction to Computer Graphics and Rendering Techniques

*  

This first block provides an overview of the Computer Graphics field and the main current challenges. It will also provide details about the open problem of Physically-Based Rendering (PBR) and the Light Transport Equation (LTE) on which we will focus during this course.

2. Block 2: Monte Carlo Methods for Physically-Based Rendering

*  

This block presents the use of Monte Carlo methods for PBR. We will see why Monte Carlo methods are needed and ubiquitous in photo-realistic image synthesis, how to improve their performance through variance reduction techniques, and the main limitations of the typical approaches.

3. Block 3: Machine Learning (ML) for Boosting Monte Carlo Methods in PBR

*  In this third block we will cover different ML-based approaches to overcome some of the limitations identified in the previous block.

 

 

Teaching methods and general organization

 

The weekly schedule of in-person activities is distributed in two hours of class that includes theory and practice.

In case mixed teaching is required by the health situation (this is the expected model):

If the health situation allows it and the necessary conditions are met, we expect to have between 50% and 70% of in-person activities. In general, when having an occupancy rate of 50%, students will attend in-person for a week and will follow class on streaming for the following week.

In case on-line teaching is required by the health situation:

The time ranges of mixed teaching are maintained but all teaching will be carried out in an online format, prioritizing synchronous sessions for the subject dynamization.

As far as possible, the gender perspective will be incorporated in the development of the subject. In addition, teachers will be attentive to those specific gender needs that students may raise, such as being able to choose a partner of the same gender if group work is carried out or being able to pose challenges against the gender gap.

 

 

Official assessment of learning outcomes

 

The course will follow a continuous evaluation consisting of:

Practical Project (60%) + Presentation and Report on a Research Paper (40%).

Students will work in groups. Marks for oral presentations, project development and submitted reports will be awarded on an individual basis.

 

 

Reading and study resources

Consulteu la disponibilitat a CERCABIB

Book

Physically Based Rendering (3rd Edition) – Pharr, Matt; Jakob, Wenzel; Humphreys, Greg; Morgan Kaufmann, 2016, ISBN: 9780128006450

  Freely available here: https://www.pbr-book.org/

Advanced Global Illumination (2nd Edition) – Dutré, Philip; Bala, Kavita; Beckaert, Philippe; A. K. Peters, Ltd., 2006, ISBN: 978-1568813073

Pattern Recognition and Machine Learning – Bishop, Christopher M.; Springer-Verlag, 2006, ISBN: 978-0-387-31073-2

Gaussian Process for Machine Learning (Adaptive Computation and Machine Learning) – Rasmussen, Carl Edward; Williams, Christopher K. I.; The MIT Press, 2005, ISBN: 978-0-262-18253-9

  Freely available here: http://www.gaussianprocess.org/gpml/chapters/RW.pdf

Efficient Quadrature Rules for Illumination Integrals: From Quasi Monte Carlo to Bayesian Monte Carlo – Marques, Ricardo; Bouville, Christian; Santos, Luís Paulo; Bouatouch, Kadi; Morgan & Claypool Publishers, 2015, ISBN: 978-1627057691

Conference papers and lectures

Machine Learning and Rendering – Keller, Alexander; Křivánek, Jaroslav; Novák, Jan; Kaplanyan, Anton; Salvi, Marco; ACM SIGGRAPH 2018 Courses (SIGGRAPH ’18). ACM, New York, NY, USA