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What does it mean to store a 3D location and hence to navigate towards it? Participants in our experiment carried out a homing task on the scale of a room (ie, 3-4m square) in an immersive virtual reality environment. In interval one, they were shown three very long coloured vertical poles from one viewing location with some head movement permitted. The poles were easily distinguishable and designed to have constant angular width irrespective of viewing distance. Participants were then transported (virtually) to another location in the scene, and in interval two they tried to navigate to the initial viewing point relative to the poles. The distributions of end-point errors on the ground plane differed significantly in shape and extent depending on pole configuration and goal location. We compared the ability of two types of models to predict these variations in the distribution of errors: (i) view-based models, based on simple features such as angles between poles from the cyclopean point, ratios of these angles, or various disparity measures and (ii) Cartesian models based on a probabilistic 3D reconstruction of the scene geometry. For our data, we find that view-based models capture important characteristics of the end-point distributions very well whereas 3D-based models fare less well. In some ways, a Cartesian model is a very particular case of a view-based model: the two are not as different from one another as they first appear.