Announcement

To participate in the re-exam, you must register before 13.09.2021, by sending an email with your name and matriculation number to Pascal Grittmann.

Overview

This advanced lecture discusses the mathematical concepts and algorithms that are used to simulate the propagation of light in a virtual scene. The topics include Monte Carlo sampling, various Global Illumination algorithms (from the basic Path Tracing algorithm to more advanced algorithms like Vertex Connection and Merging), and HDR imaging. In the practical exercises, the students implement some of the algorithms discussed in the lecture in a lightweight rendering framework.

Instructors

Teaching Assistants

Tutors

Exam

The final exam will be either written or oral, depending on the number of participants and the Covid situation. The decision will be made in May.

Pre-requisites

  • Programming experience with C++
  • Basic vector math (dot product, cross product, …)

The advanced concepts taught in this course are used on top of the basic techniques that are part of our Computer Graphics core lecture. But the RIS course is self-contained and can be followed without that background.

Lectures and assignments

Date Lecture - Instructor Resources
12.04.2021

Introduction

All

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15.04.2021

Rendering equation

Karol Myszkowski

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19.04.2021

Radiosity

Karol Myszkowski

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22.04.2021

Probability theory + MC

Pascal Grittmann

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26.04.2021

BRDFs and path tracing

Gurprit Singh

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29.04.2021

Advanced sampling

Gurprit Singh

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03.05.2021

Spatio-temporal sampling

Gurprit Singh

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06.05.2021

Bidirectional path tracing

Philipp Slusallek

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10.05.2021

Density estimation

Karol Myszkowski

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13.05.2021

Holiday

17.05.2021

Vertex connection and merging

Karol Myszkowski

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20.05.2021

Volume rendering

Gurprit Singh

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24.05.2021

Holiday

27.05.2021

Virtual point lights

Gurprit Singh

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31.05.2021

Markov chain Monte Carlo

Philipp Slusallek

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03.06.2021

Holiday

07.06.2021

Path guiding

Philipp Slusallek

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10.06.2021

AnyDSL

Philipp Slusallek

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14.06.2021

Machine Learning for Rendering I

Gurprit Singh

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17.06.2021

HDR and tone mapping

Karol Myszkowski

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21.06.2021

Perception

Karol Myszkowski

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24.06.2021

Modern display technology

Karol Myszkowski

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28.06.2021

EGSR

egsr.eu

01.07.2021

EGSR

egsr.eu

05.07.2021

Machine Learning for Rendering II

Gurprit Singh

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08.07.2021

Machine Learning for Rendering III

Gurprit Singh

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12.07.2021

Machine Learning for Rendering IV

Gurprit Singh

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15.07.2021

Radar / Spectral

Alexander Rath / Ömercan Yazici

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19.07.2021

Efficient direct lighting

Philipp Slusallek

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22.07.2021

Wrap-up

All

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General Regulations

  • Type: Special Lecture, Practical computer science
  • ECTS: 9 credit points
  • Practical assignments
    • Longer term projects
    • Not a rendering competition as in CG1
  • Assignments can be submitted by groups of up to 2 students.

Literature

The lecture is not bound to a specific book. The following list contains the most important books about image synthesis:

  • Pharr, Jakob, Humphreys, Physically Based Rendering : From Theory to Implementation, Morgan Kaufmann
  • Shirley et al., Realistic Ray Tracing, 2. Ed., AK. Peters, 2003
  • Jensen, Realistic Image Synthesis Using Photon Mapping, AK. Peters, 2001
  • Dutre, at al., Advanced Global Illumition, AK. Peters, 2003
  • Glassner, Principles of Digital Image Synthesis, 2 volumes, Morgan Kaufman, 1995
  • Cohen, Wallace, Radiosity and Realistic Image Synthesis, Academic Press, 1993
  • Apodaca, Gritz, Advanced Renderman: Creating CGI for the Motion Pictures, Morgan Kaufmann, 1999
  • Ebert, Musgrave, et al., Texturing and Modeling, 3. Ed., Morgan Kaufmann, 2003
  • Reinhard, Ward, Pattanaik, Debevec, Heidrich, Myszkowski, High Dynamic Range Imaging, Morgan Kaufmann Publishers, 2nd edition, 2010.
  • Myszkowski, Mantiuk, Krawczyk. High Dynamic Range Video. Synthesis Digital Library of Engineering and Computer Science. Morgan & Claypool Publishers, San Rafael, USA, 2008.

Here is a list of other reference materials you can use, grouped by topic: