In this work, we provide a solution to the disambiguation task by leveraging the traditional techniques of candidate mapping entity generation and local evaluation with some of the latest developments, such as word embeddings. We also consider a graph-based collective process to establish a topical relatedness metric that helps true mapping entities in a document to disambiguate one another through personalized PageRank. The final mapping entities for the given surface forms are obtained by heuristically reincorporating the candidates' local features with their resulting graph score and performing a maximal discriminant selection. The proposed methodology is capable of reaching up to 80% accuracy when it is evaluated against a well known dataset with around 18,000 named entity mentions.

We have implemented Reflective Shadow Maps (RSM), together with Percentage-Closer Soft Shadows (PCSS) and Screen-Space Ambient Occlusion (SSAO) for added realism and details. Our approach works mostly with blurred textures and diffuse 3D objects shaded with the Blinn-Phong Reflectance Model. We have also resorted to Deferred Rendering to achieve interactive rates when RSM and SSAO are enabled, which are, by definition, very expensive tasks in terms of GPU resources. Finally, with regards to random sampling, we are using Poisson Disks which provide a good even distribution of 2D points without the artifacts that usually appear with uniformly distributed numbers in both PCSS and the importance driven sampling in RSM's indirect lighting.