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UNC512: Augmented and Virtual Reality
TIET Patiala

Course Website

Instructor(s):

(RGB) Raghav B. Venkataramaiyer <bv.raghav -at- thapar -dot- edu>

(1)Academic Calendar

  1. Requires thapar.edu login

Course Syllabus

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Evaluation

Code Title Date Weightage
SESS#SEM1 Proj Eval 1 CE 10
MST Mid Sem Exam TBA 30
SESS#SEM2 Proj Eval 2 CE/ Mon Apr 20, 3:30 PM 20
EST End Sem Exam TBA 40

Semester Project

Students shall partition themselves into teams of two or three, in order to
a) propose an academic project for the semester based on the course curriculum, and
b) report the findings.

Proj Eval 1

Evaluation of the project proposal, and a mid-sem progress monitoring. This shall be conducted in continuous evaluation mode [CE].

Proj Eval 2

Intermediate progress monitoring post MST in continuous evaluation mode [CE], and Final evaluation based on a seminar, tentatively scheduled on Mon Apr 20, 3:30 PM.

Resources

  1. Project Proposal [HOWTO]
  2. Report [HOWTO]

Semester Exam (Written)

Topic(s) Covered

For topics covered in MST/ EST, check out the course content for tags [MST] and [EST] respectively

Instructions

MST/ EST shall follow the following common proforma of instructions,

  1. Word limit and marks shall be mentioned along with questions (e.g. [50w, 5M]). Non-adherence shall be penalised.
  2. Attempt all parts of a question in one space. Only the first such space shall be evaluated.
  3. Fill the answer index on the first page of the answer sheet.
  4. Exchange of stationery/ equipment is prohibited. Breach of code conduct may attract UMC.
  5. Do write your roll no. and name on the question paper. Do not write anything else.
  6. Marks shall be awarded on the basis of solutions, not steps.


Fig: Sample question in [MST]/[EST]

Choice

Typically, there is no choice in the MST and a choice of one question in the EST. But, of course, this is subject to the erstwhile institute policy.

Course Content

  1. [MST] [EST] Overview, Introduction and Open Problems.
  2. [MST] [EST] (Scribe) Virtual Reality Input and Output Modalities.
  3. [MST] [EST] The Geometric Model
  4. [EST] Visual Computation in VR.
  5. [EST] Interactive Techniques in VR.

Supplementary Course Content

[Prerequisites] Slide Courtesy: UTA027

  1. Introduction to ML
  2. Linear Regression
  3. Classification and Logistic Regression
  4. Neuron and it application in Regression/ Classification
  5. Deep Neural Networks
  6. Computer Vision Overview
  7. Computer Vision Problems
  8. Deep Learning in Computer Vision

More for the curious,

  1. Support Vector Machines

Course Learning Outcomes

The students will be able to:

  1. Analyze the components of AR and VR systems, its current and upcoming trends, types, platforms, and devices.
  2. Compare technologies in the context of AR and VR systems design.
  3. Implement various techniques and algorithms used to solve complex computing problems in AR and VR systems.
  4. Develop interactive augmented reality applications for PC and Mobile based devices using a variety of input devices.
  5. Demonstrate the knowledge of the research literature in augmented reality for both compositing and interactive applications.

Resources

  1. [CL] The Central Library (Link) (Koha Catalogue)
  2. [RR] RefRead (Link)
  3. [TB] [CL] Doug A. B., Kruijff E., LaViola J. J. and Poupyrev I. , 3D User Interfaces: Theory and Practice , Addison-Wesley (2005,2011p) 2nd ed. ISBN: 9780134034324
  4. [TB] Parisi, T. (2015). Learning Virtual Reality: Developing Immersive Experiences and Applications for Desktop, Web, and Mobile. Japan: O'Reilly Media. ISBN: 9781491922781
  5. [TB] [CL] Schmalstieg, D., Hollerer, T. (2016). Augmented Reality: Principles and Practice. United Kingdom: Pearson Education. ISBN:9780133153200
  6. [RB] Whyte, J. (2002). Virtual Reality and the Built Environment. United Kingdom: Architectural Press. ISBN: 9780750653725
  7. [RB] Aukstakalnis, S. (2017). Practical Augmented Reality: A Guide to the Technologies, Applications, and Human Factors for AR and VR. Netherlands: Addison-Wesley. ISBN: 9780134094236
  8. [CL] Bishop, C. M. (2006). Pattern recognition and machine learning. Springer. ISBN: 9788132209065
  9. [CL] Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2022). Introduction to Algorithms (Fourth). MIT Press. ISBN: 9788120340077
  10. [CL] Gareth James, Daniela Witten, Trevor Hastie, & Robert Tibshirani. (2013). An Introduction to Statistical Learning (1st ed.). Springer. DOI: 10.1007/978-1-4614-7138-7 ISBN: 9781461471387 (Link)
  11. [CL] MacKay, D. J. C. (2003). Information theory, inference and learning algorithms. Cambridge University Press. ISBN: 9780521670517 (Link)
  12. Bertsekas, D., & Tsitsiklis, J. N. (2008). Introduction to probability (Vol. 1). Athena Scientific. ISBN: 9781886529236 (Google Scholar)
  13. [YT] [MOOC] Introduction to Probability. (MIT-OCW) (Archive 2011) (Archive 2018)
  14. [YT] [MOOC] Algorithms Illuminated. by Tim Roughgarden Videos: Part 1 Basics, Videos: Part 2 Graphs and Official Website
  15. [RB] [CL] Jurafsky, D., & Martin, J. H. (2025, January). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. ISBN: 9789332518414 (The Book), (The Chapter on Logistic Regression), (Official Website)
  16. [MOOC] Illinois Institute Page on Logistic Regression.
  17. [YT] Late Prof. Winston’s Lecture on SVM (MIT-OCW) Video by MIT-OCW

  1. Each assignment carries weightage of 2 marks ↩

  2. Some of the assignments are competition and leaderboard based; Marks awarded shall be based on rank on leaderboard. ↩

  3. A01-05 A01-03 shall reflect on the webkiosk as consolidated SESS#A1 (MM:10); and similary A06-10 A04-07 as SESS#A2 (MM:10). ↩