Saliency prediction in 360° architectural scenes: Performance and impact of daylight variations
Saliency models are image-based prediction models that estimate human visual attention. Such models, when applied to architectural spaces, could pave the way for design decisions where visual attention is taken into account. In this study, we tested the performance of eleven commonly used saliency m...
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Published in | Journal of environmental psychology Vol. 92; p. 102110 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
Elsevier Ltd
01.12.2023
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Subjects | |
Online Access | Get full text |
ISSN | 0272-4944 1522-9610 |
DOI | 10.1016/j.jenvp.2023.102110 |
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Abstract | Saliency models are image-based prediction models that estimate human visual attention. Such models, when applied to architectural spaces, could pave the way for design decisions where visual attention is taken into account. In this study, we tested the performance of eleven commonly used saliency models that combine traditional and deep learning methods on 126 rendered interior scenes with associated head tracking data. The data was extracted from three experiments conducted in virtual reality between 2016 and 2018. Two of these datasets pertain to the perceptual effects of daylight and include variations of daylighting conditions for a limited set of interior spaces, thereby allowing to test the influence of light conditions on human head movement. Ground truth maps were extracted from the collected head tracking logs, and the prediction accuracy of the models was tested via the correlation coefficient between ground truth and prediction maps. To address the possible inflation of results due to the equator bias, we conducted complementary analyses by restricting the area of investigation to the equatorial image regions. Although limited to immersive virtual environments, the promising performance of some traditional models such as GBVS360eq and BMS360eq for colored and textured architectural rendered spaces offers us the prospect of their possible integration into design tools. We also observed a strong correlation in head movements for the same space lit by different types of sky, a finding whose generalization requires further investigations based on datasets more specifically developed to address this question.
•We tested the performance of 11 saliency models on 126 interior scenes with associated head tracking data.•The data was extracted from three experiments conducted in virtual reality.•GBVS360eq and BMS360eq showed a promising performance for colored and textured architectural spaces.•We observed a strong correlation in head movements for the same space rendered for different daylight conditions. |
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AbstractList | Saliency models are image-based prediction models that estimate human visual attention. Such models, when applied to architectural spaces, could pave the way for design decisions where visual attention is taken into account. In this study, we tested the performance of eleven commonly used saliency models that combine traditional and deep learning methods on 126 rendered interior scenes with associated head tracking data. The data was extracted from three experiments conducted in virtual reality between 2016 and 2018. Two of these datasets pertain to the perceptual effects of daylight and include variations of daylighting conditions for a limited set of interior spaces, thereby allowing to test the influence of light conditions on human head movement. Ground truth maps were extracted from the collected head tracking logs, and the prediction accuracy of the models was tested via the correlation coefficient between ground truth and prediction maps. To address the possible inflation of results due to the equator bias, we conducted complementary analyses by restricting the area of investigation to the equatorial image regions. Although limited to immersive virtual environments, the promising performance of some traditional models such as GBVS360eq and BMS360eq for colored and textured architectural rendered spaces offers us the prospect of their possible integration into design tools. We also observed a strong correlation in head movements for the same space lit by different types of sky, a finding whose generalization requires further investigations based on datasets more specifically developed to address this question.
•We tested the performance of 11 saliency models on 126 interior scenes with associated head tracking data.•The data was extracted from three experiments conducted in virtual reality.•GBVS360eq and BMS360eq showed a promising performance for colored and textured architectural spaces.•We observed a strong correlation in head movements for the same space rendered for different daylight conditions. Saliency models are image-based prediction models that estimate human visual attention. Such models, when applied to architectural spaces, could pave the way for design decisions where visual attention is taken into account. In this study, we tested the performance of eleven commonly used saliency models that combine traditional and deep learning methods on 126 rendered interior scenes with associated head tracking data. The data was extracted from three experiments conducted in virtual reality between 2016 and 2018. Two of these datasets pertain to the perceptual effects of daylight and include variations of daylighting conditions for a limited set of interior spaces, thereby allowing to test the influence of light conditions on human head movement. Ground truth maps were extracted from the collected head tracking logs, and the prediction accuracy of the models was tested via the correlation coefficient between ground truth and prediction maps. To address the possible inflation of results due to the equator bias, we conducted complementary analyses by restricting the area of investigation to the equatorial image regions. Although limited to immersive virtual environments, the promising performance of some traditional models such as GBVS360eq and BMS360eq for colored and textured architectural rendered spaces offers us the prospect of their possible integration into design tools. We also observed a strong correlation in head movements for the same space lit by different types of sky, a finding whose generalization requires further investigations based on datasets more specifically developed to address this question. |
ArticleNumber | 102110 |
Author | Kim, Seungryong Süsstrunk, Sabine Aydemir, Bahar Andersen, Marilyne Karmann, Caroline Chamilothori, Kynthia |
Author_xml | – sequence: 1 givenname: Caroline orcidid: 0000-0003-3328-7936 surname: Karmann fullname: Karmann, Caroline email: caroline.karmann@kit.edu organization: Laboratory of Integrated Performance in Design (LIPID), École Polytechnique Fédérale de Lausanne (EPFL), Switzerland – sequence: 2 givenname: Bahar surname: Aydemir fullname: Aydemir, Bahar organization: Image and Visual Representation Lab (IVRL), École Polytechnique Fédérale de Lausanne (EPFL), Switzerland – sequence: 3 givenname: Kynthia surname: Chamilothori fullname: Chamilothori, Kynthia organization: Laboratory of Integrated Performance in Design (LIPID), École Polytechnique Fédérale de Lausanne (EPFL), Switzerland – sequence: 4 givenname: Seungryong surname: Kim fullname: Kim, Seungryong organization: Image and Visual Representation Lab (IVRL), École Polytechnique Fédérale de Lausanne (EPFL), Switzerland – sequence: 5 givenname: Sabine surname: Süsstrunk fullname: Süsstrunk, Sabine organization: Image and Visual Representation Lab (IVRL), École Polytechnique Fédérale de Lausanne (EPFL), Switzerland – sequence: 6 givenname: Marilyne surname: Andersen fullname: Andersen, Marilyne organization: Laboratory of Integrated Performance in Design (LIPID), École Polytechnique Fédérale de Lausanne (EPFL), Switzerland |
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Cites_doi | 10.1191/1365782802li030oa 10.2190/EM.30.1.c 10.1109/34.730558 10.1016/j.jenvp.2022.101839 10.1016/S0360-1323(02)00118-X 10.1109/TVCG.2018.2793599 10.3390/ijgi8010011 10.1016/j.visres.2011.07.002 10.1007/s10339-007-0199-2 10.1016/j.visres.2015.03.005 10.1371/journal.pone.0169997 10.1177/1477153516644292 10.1109/TMM.2019.2939744 10.1177/14771535211055703 10.1016/0272-4944(95)90001-2 10.1016/j.neunet.2020.05.004 10.1016/j.buildenv.2019.04.035 10.1080/13506280902916691 10.1016/j.jenvp.2018.09.001 10.1080/15230406.2019.1697965 10.1177/1477153516649016 10.1016/j.sbspro.2011.10.231 10.1016/j.jenvp.2018.12.005 10.1016/j.landurbplan.2015.12.007 10.1037/a0015808 |
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Keywords | Head tracking Daylight Indoor spaces Visual attention Saliency prediction modelling |
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