University of East Anglia Featured PhD Programmes
Xi’an Jiaotong-Liverpool University Featured PhD Programmes
Lancaster University Featured PhD Programmes

Dynamic Scene Reconstruction for Virtual Reality Video

This project is no longer listed on and may not be available.

Click here to search for PhD studentship opportunities
  • Full or part time
    Dr Christian Richardt
    Dr Neill Campbell
  • Application Deadline
    No more applications being accepted
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

The goal of this project is to reconstruct the geometry and appearance of dynamic real-world environments to enable more immersive virtual reality video experiences.

State-of-the-art VR video approaches (e.g. Anderson et al., 2016) produce stereoscopic 360° video, which comprises separate 360° videos for the left and right eye (like 3D movies, but in 360°). The videos can, for example, be viewed on YouTube using a VR headset such as Google Cardboard or Daydream. Unfortunately, such videos only allow viewers to look in different directions, but they do not respond to any head motion such as moving left/right, forward/backwards or up/down. Truly immersive VR video, on the other hand, requires ‘freedom of motion’ in six degrees-of-freedom (‘6-DoF’), so that viewers see the correct views of an environment regardless of where they are (3 DoF) and where they are looking (+3 DoF).

This project aims to develop novel dynamic scene reconstruction techniques that are capable of producing temporally-coherent, dense, textured, time-varying 3D geometry from dynamic real-world environments from one or more standard or 360-degree video cameras. In particular, the goal is to convincingly reconstruct the visual dynamics of the real world, such as people and moving animals or plants, so that the reconstructed dynamic geometry can provide the foundation for a novel video-based rendering approach that synthesises visually plausible novel views with 6 degrees-of-freedom for the specific head position and orientation of a viewer in VR. This experience will provide correct motion parallax and depth perception to the viewer (like Luo et al., 2018) to ensure unparalleled realism and immersion.

Candidates should normally have a good first degree (equivalent to a First Class or 2:1 Honours), or a Master’s degree in computer science, visual computing or a related discipline. A strong mathematical background and strong previous programming experience, preferably in C++ and/or Python, is required. Candidates must have a strong interest in visual computing, and previous experience in computer vision, computer graphics and image processing is highly desirable.

Informal enquiries should be directed to Dr Christian Richardt, [Email Address Removed]

Formal applications should be made via the University of Bath’s online application form for a PhD in Computer Science:

More information about applying for a PhD at Bath may be found here:

Anticipated start date: 1 October 2018

Funding Notes

UK and EU students applying for this project may be considered for a University Research Studentship which will cover Home/EU tuition fees, a training support fee of £1,000 per annum and a tax-free maintenance allowance at the RCUK Doctoral Stipend rate (£14,777 in 2018-19) for a period of 3.5 years.

Note: ONLY UK and EU applicants are eligible for this studentship; unfortunately, applicants who are classed as Overseas for fee paying purposes are NOT eligible for funding.


R. Anderson, D. Gallup, J. T. Barron, J. Kontkanen, N. Snavely, C. Hernandez, S. Agarwal and S. M. Seitz, “Jump: Virtual Reality Video”. ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2016).

B. Luo, F. Xu, C. Richardt, J.-H. Yong, “Parallax360: Stereoscopic 360° Scene Representation for Head-Motion Parallax”. IEEE Transactions on Visualization and Computer Graphics (IEEE VR 2018).

How good is research at University of Bath in Computer Science and Informatics?

FTE Category A staff submitted: 24.00

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

FindAPhD. Copyright 2005-2020
All rights reserved.