Computer Graphics

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 geometric calibration method to accurately register a galvanoscopic scanning laser projection system (GLP) based on 2D vector input data onto an arbitrarily complex 3D-shaped projection surface.

We present a robust, unbiased technique for intelligent light-path construction in path-tracing algorithms.

The hierarchical and heterogeneous nature of human crowd behaviors makes it very difficult to find a definitive set of evaluation rules or empirical metrics. Therefore, data-driven evaluation methods are particularly useful for this purpose.

By using non-parametric Bayesian methods, we learn coupled spatial and temporal patterns with minimum prior knowledge.

We address the problem of denoising Monte Carlo renderings by studying existing approaches and proposing a new algorithm that yields state-of-the-art performance on a wide range of scenes.

We present a method for projecting arbitrary color images using a white light source and an optical device with no colored components—consisting solely of one or two prisms and two transparent masks.