MAP3D: An explorative approach for automatic mapping of real-world eye-tracking data on a virtual 3D model.

Abstract

Mobile eye tracking helps to investigate real-world settings, in which participants can move freely. This enhances the studies' ecological validity but poses challenges for the analysis. Often, the 3D stimulus is reduced to a 2D image (reference view) and the fixations are manually mapped to this 2D image. This leads to a loss of information about the three-dimensionality of the stimulus. Using several reference images, from different perspectives, poses new problems, in particular concerning the mapping of fixations in the transition areas between two reference views. A newly developed approach (MAP3D) is presented that enables generating a 3D model and automatic mapping of fixations to this virtual 3D model of the stimulus. This avoids problems with the reduction to a 2D reference image and with transitions between images. The x, y and z coordinates of the fixations are available as a point cloud and as .csv output. First exploratory application and evaluation tests are promising: MAP3D offers innovative ways of post-hoc mapping fixation data on 3D stimuli with open-source software and thus provides cost-efficient new avenues for research.