Background: How do we recognize visually perceived three-dimensional o
bjects, particularly when they are seen from novel view-points? Recent
psychophysical studies have suggested that the human visual system ma
y store a relatively small number of two-dimensional views of a three-
dimensional object, recognizing novel views of the object by interpola
tion between the stored sample views. In order to investigate the neur
al mechanisms underlying this process, physiological experiments are r
equired and, as a prelude to such experiments, we have been interested
to know whether the observations made with human observers extend to
monkeys. Results: We trained monkeys to recognize computer-generated i
mages of objects presented from an arbitrarily chosen training view an
d containing sufficient three-dimensional information to specify the o
bject's structure. We subsequently tested the trained monkeys' ability
to generalize recognition of the object to views generated by rotatio
n of the target object around any arbitrary axis. The monkeys recogniz
ed as the target only those two-dimensional views that were close to t
he familiar, training view. Recognition became increasingly difficult
for the monkeys as the stimulus was rotated away from the experienced
viewpoint, and failed for views farther than about 40 degrees from the
training view. This suggests that, in the early stages of learning to
recognize a previously unfamiliar object, the monkeys build two-dimen
sional, viewer-centered object representations, rather than a three-di
mensional model of the object. When the animals were trained with as f
ew as three views of the object, 120 degrees apart, they could often r
ecognize all the views of the object resulting from rotations around t
he same axis. Conclusion: Our experiments show that recognition of thr
ee-dimensional novel objects is a function of the object's retinal pro
jection. This suggests that nonhuman primates, like humans, may accomp
lish view-invariant recognition of familiar objects by a viewer-center
ed system that interpolates between a small number of stored views. Th
e measures of recognition performance can be simulated by a regulariza
tion network that stores a few familiar views, and is endowed with the
ability to interpolate between these views. Our results provide the b
asis for physiological studies of object-recognition by monkeys and su
ggest that the insights gained from such studies should apply also to
humans.