Advanced Computer Graphics

Module aims

To introduce the students to modern techniques in realistic computer graphics and image synthesis, particularly image-based techniques for photorealism.

Learning outcomes

Learning Outcomes -- Knowledge and Understanding

(1) understand the mathematical foundations of radiometry
(2) understand the image formation model of digital photography and HDR imaging
(3) understand the principles of image-based lighting and rendering
(4) understand the principles of BRDF models and reflectance measurement techniques
(5) understand the basics of Monte Carlo techniques for global illumination
(6) understand the principles of light scattering in various media including human skin
(7) understand the current research issues in realistic computer graphics including emerging machine learning applications.

Learning Outcomes - Intellectual Skills

(1) reading and understanding research literature in computer graphics
(2) relating fundamentals of image synthesis with fundamentals of signal processing
(3) analysing practical constraints besides theoretical limits of various computational methods and imaging techniques

Learning Outcomes - Practical Skills

(1) learning practical use of mathematical techniques
(2) programming basic applications in digital photography and image based lighting
(3) make effective use of current computing and photography hardware and software systems for image synthesis

Learning Outcomes - Transferable Skills

(1) To relate abstract mathematical concepts with concrete real world applications and physical phenomena
(2) To relate physical/biological/optical phenomena to technological applications in computing and imaging

Module syllabus


1) High dynamic range imaging: HDR photography, tools for image-based computer graphics, light probes.

2) Image-based lighting: HDRI environmental illumination, light stage, reflectance fields.

3) Physics of light: radiometry, bidirectional reflectance distribution function (BRDF), geometric optics, reflection models.

4) Monte Carlo sampling: Monte Carlo integration, probability distribution function (PDF), sampling techniques for BRDFs, and illumination models.

5) Global illumination: Monte Carlo path tracing, Metropolis light transport, irradiance caching, photon mapping.

6) Scattering: radiative transfer and diffusion of light, subsurface scattering, scattering in participating media.

7) Scattering in faces and hair: Measurement and modeling of multi-layerd skin reflectance models, hair fibre scattering models.

8) Measured material representations: measured BRDFs, spatially varying BRDFs.

9) Light fields: representation, acquisition, modeling.

10) Machine learning applications in realistic rendering. 


The contents of (317) Graphics

Teaching methods

Primarily classroom lecture with slides, with some explanation on white board. The teaching material is augmented with several tutorials and two programming assignments for students to get some hands-on experience with the material.


Combined weighting between two assignments that will consistute 33% of the grade and the final exam that will consitute 67% of the grade. For the final exam, students will have to attempt 2 out of 3 given questions.

Reading list

Module leaders

Dr Abhijeet Ghosh