Visual Computing

We present a new method for decomposing an image into a set of soft color segments that are analogous to color layers with alpha channels that have been commonly utilized in modern image manipulation software.

We investigate how to generalize this concept to non-invertible sampling techniques commonly found in practice, and introduce probabilistic inverses that extend our perturbation to cover most sampling methods found in light transport simulations.

We propose a novel pre-filtering method that reduces the noise introduced by depth-of-field and motion blur effects in geometric buffers (G-buffers) such as texture, normal and depth images.

We investigate the advantages of a stereo, multi-spectral acquisition system for material classication in ground-level landscape images.

The work focuses on evaluating responses to a selection of synthesized camera oriented reality mixing techniques for AR, such as motion blur, defocus blur, latency and lighting responsiveness.

We present a low-cost solution for yaw drift in head-mounted display systems that performs better than current commercial solutions and provides a wide capture area for pose tracking.

We present a system for rapid acquisition of bespoke, animatable, full-body avatars including face texture and shape.

The interactive narrative guides guests through the immersive story with lighting and spatial audio design and integrates both walkable and air haptic actuators.

We believe our work is a step towards making captured data more accessible to artists for foliage modelling

We propose an efficient STEA filtering pipeline based on the recently proposed permeability filter (PF) which offers high quality and halo reduction capabilities.