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Modelling human visual navigation using multi-view scene reconstruction
It is often assumed that humans generate a 3D reconstruction of the environment, either in egocentric or world-based coordinates, but the steps involved are unknown. Here, we propose two reconstruction-based models, evaluated using data from two tasks in immersive virtual reality. We model the obser...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3755223/ https://www.ncbi.nlm.nih.gov/pubmed/23778937 http://dx.doi.org/10.1007/s00422-013-0558-2 |
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author | Pickup, Lyndsey C. Fitzgibbon, Andrew W. Glennerster, Andrew |
author_facet | Pickup, Lyndsey C. Fitzgibbon, Andrew W. Glennerster, Andrew |
author_sort | Pickup, Lyndsey C. |
collection | PubMed |
description | It is often assumed that humans generate a 3D reconstruction of the environment, either in egocentric or world-based coordinates, but the steps involved are unknown. Here, we propose two reconstruction-based models, evaluated using data from two tasks in immersive virtual reality. We model the observer’s prediction of landmark location based on standard photogrammetric methods and then combine location predictions to compute likelihood maps of navigation behaviour. In one model, each scene point is treated independently in the reconstruction; in the other, the pertinent variable is the spatial relationship between pairs of points. Participants viewed a simple environment from one location, were transported (virtually) to another part of the scene and were asked to navigate back. Error distributions varied substantially with changes in scene layout; we compared these directly with the likelihood maps to quantify the success of the models. We also measured error distributions when participants manipulated the location of a landmark to match the preceding interval, providing a direct test of the landmark-location stage of the navigation models. Models such as this, which start with scenes and end with a probabilistic prediction of behaviour, are likely to be increasingly useful for understanding 3D vision. |
format | Online Article Text |
id | pubmed-3755223 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-37552232013-09-05 Modelling human visual navigation using multi-view scene reconstruction Pickup, Lyndsey C. Fitzgibbon, Andrew W. Glennerster, Andrew Biol Cybern Original Paper It is often assumed that humans generate a 3D reconstruction of the environment, either in egocentric or world-based coordinates, but the steps involved are unknown. Here, we propose two reconstruction-based models, evaluated using data from two tasks in immersive virtual reality. We model the observer’s prediction of landmark location based on standard photogrammetric methods and then combine location predictions to compute likelihood maps of navigation behaviour. In one model, each scene point is treated independently in the reconstruction; in the other, the pertinent variable is the spatial relationship between pairs of points. Participants viewed a simple environment from one location, were transported (virtually) to another part of the scene and were asked to navigate back. Error distributions varied substantially with changes in scene layout; we compared these directly with the likelihood maps to quantify the success of the models. We also measured error distributions when participants manipulated the location of a landmark to match the preceding interval, providing a direct test of the landmark-location stage of the navigation models. Models such as this, which start with scenes and end with a probabilistic prediction of behaviour, are likely to be increasingly useful for understanding 3D vision. Springer Berlin Heidelberg 2013-06-19 2013 /pmc/articles/PMC3755223/ /pubmed/23778937 http://dx.doi.org/10.1007/s00422-013-0558-2 Text en © The Author(s) 2013 https://creativecommons.org/licenses/by/2.0/ Open AccessThis article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited. |
spellingShingle | Original Paper Pickup, Lyndsey C. Fitzgibbon, Andrew W. Glennerster, Andrew Modelling human visual navigation using multi-view scene reconstruction |
title | Modelling human visual navigation using multi-view scene reconstruction |
title_full | Modelling human visual navigation using multi-view scene reconstruction |
title_fullStr | Modelling human visual navigation using multi-view scene reconstruction |
title_full_unstemmed | Modelling human visual navigation using multi-view scene reconstruction |
title_short | Modelling human visual navigation using multi-view scene reconstruction |
title_sort | modelling human visual navigation using multi-view scene reconstruction |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3755223/ https://www.ncbi.nlm.nih.gov/pubmed/23778937 http://dx.doi.org/10.1007/s00422-013-0558-2 |
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