A Systematic Review of Visualization Techniques and Analysis Tools for Eye-Tracking in 3D Environments.

This systematic literature review presents an update on developments in 3D visualization techniques and analysis tools for eye movement data in 3D environments. With the introduction of affordable and non-intrusive eye-tracking solutions to the mass market, access to users' gaze is now increasingly possible. As a result, the adoption of eye-tracking in virtual environments using head-mounted displays is expected to increase since the trend is to incorporate gaze tracking as part of new technical solutions.

Scale Space Graph Representation and Kernel Matching for Non Rigid and Textured 3D Shape Retrieval.

In this paper we introduce a novel framework for 3D object retrieval that relies on tree-based shape representations (TreeSha) derived from the analysis of the scale-space of the Auto Diffusion Function (ADF) and on specialized graph kernels designed for their comparison. By coupling maxima of the Auto Diffusion Function with the related basins of attraction, we can link the information at different scales encoding spatial relationships in a graph description that is isometry invariant and can easily incorporate texture and additional geometrical information as node and edge features. Using custom graph kernels it is then possible to estimate shape dissimilarities adapted to different specific tasks and on different categories of models, making the procedure a powerful and flexible tool for shape recognition and retrieval.