  This paper proves that visual object recognition systems using only 2D
Euclidean similarity measurements to compare object views against previously
seen views can achieve the same recognition performance as observers having
access to all coordinate information and able of using arbitrary 3D models
internally. Furthermore, it demonstrates that such systems do not require more
training views than Bayes-optimal 3D model-based systems. For building computer
vision systems, these results imply that using view-based or appearance-based
techniques with carefully constructed combination of evidence mechanisms may
not be at a disadvantage relative to 3D model-based systems. For computational
approaches to human vision, they show that it is impossible to distinguish
view-based and 3D model-based techniques for 3D object recognition solely by
comparing the performance achievable by human and 3D model-based systems.}
