Rendering

Lecture Announcements

Registration and course data is available on the TISS site.

Communication with students, Q&A sessions, and some extra material is provided through our TUWEL site! It'll be updated throughout the semester. You should subscribe to the announcement forum, or make sure to read it. Accounts for Uni Wien students are under way.

This year we will hold the lecture together with Prof. Torsten Möller from Uni Wien. He will offer additional content in accordance with his lecture being 6 ECTS. This additional lecture content will be optional, but highly recommended for our students. Uni Wien students will use our submission system, our TUWEL / Moodle, but will have additional assignments and take the exam from Torsten. TU Wien students can take the additional assignments, do an extended exam and get additional credits via this course. We will update this website soon with additional information.

Properties

  • Semester hours: 2.0
  • Credits: 3.0
  • Type: VU Lecture and Exercise

Aim of the Course

This course will teach you how to write a physically correct and unbiased renderer based on the path tracing algorithm.

In the beginning, you will learn about the physics and math of light transport. Closely connected is the rendering equation, a high-dimensional integral describing the equilibrium of photons in a scene. We will then show you how to compute such integrals using Monte Carlo methods and to apply this new knowledge to implement the recursive path-tracing algorithm. At this point, you will have an understanding of how rendering works, but a lot remains to be learned: The asymptotic complexity of ray-tracing can be reduced by using acceleration data structures, enabling the program to deal with scenes that consist of more than just a dozen triangles. Materials like plastic, glass, metal, paint, and skin have properties that need special considerations during implementation. And finally, we will teach a bit about HDR, tone mapping, measuring error, and other rendering pipeline details.

The exercises will give you an understanding of the principles of Monte Carlo Integration, the rendering equation, optimization techniques, and material modelling. There will be many bonus tasks for interested students. We will also have a performance and a scene/bonus-task competition.

Schedule, Contents and Lecture Slides

The lecture slides will be linked in this table once they are available (after the lecture). You can also look at the lecture from last year on YouTube, but recall that this years lecture will be different and hopefully improved. This year the lecture will be live, videos will be published on TUWEL only.

Date Tuesday Thursday
01-Mar 03-Mar Introduction (BK) Comprehensive overview (TM) Part1 Part2
08-Mar 10-Mar Light (Radiometry / Photometry) (TM) Image Pipeline (TM)
15-Mar 17-Mar Monte Carlo Integration (AC BK) Nori Introduction / FAQ (AC BK)
Deadline 20-Mar Assignment 0: Setting up Nori
22-Mar 24-Mar Rendering Equation (AC BK) Biased Rendering Algorithms (TM)
29-Mar 31-Mar Path Tracing Basics (BK) Biased Rendering Algorithms (TM)
05-Apr 07-Apr Spatial Acceleration Structures (BK) Participating Media (TM)
12-Apr 14-Apr Easter
Deadline 17-Apr Assignment 1: Path Tracing
19-Apr 21-Apr Easter
26-Apr 28-Apr Importance Sampling (BK AC)  
03-May 05-May MIS / Next Event Estimation (AC, Part1, Part2, Part3) Radiosity (BK)
Deadline 08-May Assignment 2: BVH Building & Traversal
10-May 12-May Reflectance Function (TM)  
17-May 19-May Reflectance Functions (TM) Reflectance Functions (TM)
Deadline 25-May Assignment 3: Materials and Importance Sampling
24-May 26-May Accessories (AC) Christi Himmelfahrt
31-May 02-Jun Bi-directional PT, Metropolis Light Transport (TM) Sampling (Fourier Transform, Aliasing vs. Noise) (TM)
07-Jun 09-Jun Pfingstdienstag Uniform / Non-uniform Sampling (TM)
14-Jun 16-Jun   Fronleichnam
21-Jun 23-Jun    
Deadline 26-Jun Final Project
28-Jun 30-Jun Final Exam

Policy on Academic Honesty

DO NOT CHEAT!

All the individual labs, homeworks and other assignments are meant to be done by each student on their own. If we think cheating might have occurred, this might have very negative consequences for you. Citing the Studienpräses (in German): "Ein 'Erschleichen' liegt vor, wenn eine fremde Leistung als Eigenleistung dargestellt wird (z.B. Textteile von anderen Personen ohne entsprechende Kenntlichmachung übernommen worden sind). [...] Die gesamte PI-LV ist nicht zu beurteilen und ein 'X' in i3v mit dem entsprechenden Vermerk 'geschummelt / erschlichen' zu erfassen."

Discussion about labs is encouraged, but please do your own work (unless the task is specifically designed as a group project). Near duplicate assignments will be considered cheating unless the assignment was restrictive enough to justify such similarities in independent work. Just think of it that way: cheating impedes learning and having fun. Please also note that opportunity makes thieves: It is your responsibility to protect your work and to ensure that it is not turned in by anyone else.

We will make use of automated plagiarism and code checking tools.

If source code (to enhance) or other material from the WWW is used, it should be acknowledged and a priori permission must be sought. Taking source code (or text/math) from a peer, the WWW, or a book to complete a lab makes you culpable of plagiarism ('X' with 'geschummelt / erschlichen'). Please note, that it is of course also not okay to copy large parts or even the whole code from somewhere else, even though you acknowledged it. This is like copying and pasting a book, putting your name on the cover, and hoping that nobody will find the fine-prints saying that it is actually from somebody else. If unclear, please talk to us before the submission. Justifications in hindsight are not possible.

Some examples of things that are not okay and will lead to an 'X':

  • Give code (or text/math) to another student
  • Give screenshot of code (or text/math) to another student
  • Copy code (or text/math) from somebody else
  • Copy code (or text/math) from the WWW without our explicit permission
  • Create code (or text/math) for others
  • Let other people create code (or text/math) for yourself

Further Reading