Content-Aware 3D Reconstruction with Gaze Data

Schöning, Julius; Jiang, Xianta; Menon, Carlo; Heidemann, Gunther

doi:10.1109/cybconf.2017.7985769

Cited by 3 publications

(3 citation statements)

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“…It was not intended to compare patterns of visual attention across several subjects on a common reference. Schöning et al (2017) also combined SfM with eye tracking. They presented a 3D reconstruction pipeline in their work that implements content awareness by combining a world camera of an eye tracker with gaze information.…”

Section: Photogrammetry and Eye Trackingmentioning

confidence: 99%

“…The goal of their work was to identify AOIs within the video sequences. Their field of research is the area of computer vision to develop new ways for assistive technologies and human-robot interaction (Schöning et al, 2017). Jensen et al (2017) also used SfM.…”

Section: Photogrammetry and Eye Trackingmentioning

confidence: 99%

“…have dealt with dynamic scenes in their marker and Microsoft Kinect-based approach. Moving objects were not the focus of our exploration, but interested readers are referred to Schöning et al (2017), who offer solutions for photogrammetry-based approaches. They created accurate 3D models out of video data including moving objects.…”

Section: Limitations Challenges and Possible Solutionsmentioning

confidence: 99%

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

Stein

Jossberger

Gruber

2023

JEMR

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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.

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Section: Photogrammetry and Eye Trackingmentioning

confidence: 99%

Section: Photogrammetry and Eye Trackingmentioning

confidence: 99%

Section: Limitations Challenges and Possible Solutionsmentioning

confidence: 99%

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