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Modelling human visual navigation using multi-view scene reconstruction.

Pickup LC, Fitzgibbon AW, Glennerster A - Biol Cybern (2013)

Bottom Line: Participants viewed a simple environment from one location, were transported (virtually) to another part of the scene and were asked to navigate back.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.

View Article: PubMed Central - PubMed

Affiliation: School of Psychology and Clinical Language Sciences, University of Reading, Reading, RG6 6AL, UK. l.c.pickup@reading.ac.uk

ABSTRACT
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.

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Four examples of navigation-error data, shown as a plan view in a 4 m  4 m box. The magenta pluses indicate points in the room which subjects reported as being the same as the goal point (black dot). The distribution of these points depends on the geometry of the condition: those with a small visual angle between poles tend to have a more “radial” distribution, e.g. conditions a and c, where the green and blue poles were seen as being close together when viewed from the goal point. In conditions b and d, the poles appear more uniformly spaced, and the recorded end-points are more dispersed
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Fig2: Four examples of navigation-error data, shown as a plan view in a 4 m 4 m box. The magenta pluses indicate points in the room which subjects reported as being the same as the goal point (black dot). The distribution of these points depends on the geometry of the condition: those with a small visual angle between poles tend to have a more “radial” distribution, e.g. conditions a and c, where the green and blue poles were seen as being close together when viewed from the goal point. In conditions b and d, the poles appear more uniformly spaced, and the recorded end-points are more dispersed

Mentions: The main results of Experiment 1 are shown at the end of the paper in Figs. 11 and 12 where they can be interpreted in relation to the modelling which is described in subsequent sections. However, Fig. 2 illustrates a portion of the data and shows what the main characteristics are that need to be modelled. The black dot shows the goal point to which participants had to return in the homing interval, and the crosses show their end-points. It is clear that the spatial distribution of end-points is affected by the layout of the poles. Figure 2c, d are extreme examples. In (c), the spread of points is mainly along the line joining the goal point and the central pole, while in (d) the pattern in reversed.Fig. 2


Modelling human visual navigation using multi-view scene reconstruction.

Pickup LC, Fitzgibbon AW, Glennerster A - Biol Cybern (2013)

Four examples of navigation-error data, shown as a plan view in a 4 m  4 m box. The magenta pluses indicate points in the room which subjects reported as being the same as the goal point (black dot). The distribution of these points depends on the geometry of the condition: those with a small visual angle between poles tend to have a more “radial” distribution, e.g. conditions a and c, where the green and blue poles were seen as being close together when viewed from the goal point. In conditions b and d, the poles appear more uniformly spaced, and the recorded end-points are more dispersed
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3755223&req=5

Fig2: Four examples of navigation-error data, shown as a plan view in a 4 m 4 m box. The magenta pluses indicate points in the room which subjects reported as being the same as the goal point (black dot). The distribution of these points depends on the geometry of the condition: those with a small visual angle between poles tend to have a more “radial” distribution, e.g. conditions a and c, where the green and blue poles were seen as being close together when viewed from the goal point. In conditions b and d, the poles appear more uniformly spaced, and the recorded end-points are more dispersed
Mentions: The main results of Experiment 1 are shown at the end of the paper in Figs. 11 and 12 where they can be interpreted in relation to the modelling which is described in subsequent sections. However, Fig. 2 illustrates a portion of the data and shows what the main characteristics are that need to be modelled. The black dot shows the goal point to which participants had to return in the homing interval, and the crosses show their end-points. It is clear that the spatial distribution of end-points is affected by the layout of the poles. Figure 2c, d are extreme examples. In (c), the spread of points is mainly along the line joining the goal point and the central pole, while in (d) the pattern in reversed.Fig. 2

Bottom Line: Participants viewed a simple environment from one location, were transported (virtually) to another part of the scene and were asked to navigate back.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.

View Article: PubMed Central - PubMed

Affiliation: School of Psychology and Clinical Language Sciences, University of Reading, Reading, RG6 6AL, UK. l.c.pickup@reading.ac.uk

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus