PDF(Pubmed)
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.
摘要：
移动眼动追踪有助于调查真实世界的设置，参与者可以自由移动。这增强了研究的生态有效性，但对分析提出了挑战。通常,例如,3D刺激被减少到2D图像(参考视图),并且注视被手动地映射到该2D图像。这导致关于刺激的三维性的信息丢失。使用多个参考图像，从不同的角度来看，带来了新的问题，特别是关于两个参考视图之间的过渡区域中的固定映射。提出了一种新开发的方法（MAP3D），该方法可以生成3D模型并将注视自动映射到刺激的虚拟3D模型。这避免了减少到2D参考图像以及图像之间的过渡的问题。x,固定的y和z坐标可以作为点云和作为。csv输出。首次探索性应用和评估测试很有希望：MAP3D通过开源软件提供了对3D刺激进行事后映射固定数据的创新方法，从而为研究提供了具有成本效益的新途径。
