  In this article, for the first time, one develops a nonparametric methodology
for an analysis of shapes of configurations of landmarks on real 3D objects
from regular camera photographs, thus making 3D shape analysis very accessible.
A fundamental result in computer vision by Faugeras (1992), Hartley, Gupta and
Chang (1992) is that generically, a finite 3D configuration of points can be
retrieved up to a projective transformation, from corresponding configurations
in a pair of camera images. Consequently, the projective shape of a 3D
configuration can be retrieved from two of its planar views. Given the inherent
registration errors, the 3D projective shape can be estimated from a sample of
photos of the scene containing that configuration. Projective shapes are here
regarded as points on projective shape manifolds. Using large sample and
nonparametric bootstrap methodology for extrinsic means on manifolds, one gives
confidence regions and tests for the mean projective shape of a 3D
configuration from its 2D camera images.
