S.B. Kang and R. Szeliski, ``3-D scene data recovery using
omnidirectional multibaseline stereo,'' to appear in
International Journal of Computer Vision, 1997.
A traditional approach to extracting geometric information from a large scene
is to compute multiple 3-D depth maps from stereo
pairs or direct range finders, and then to merge the 3-D data.
However, the resulting merged depth maps may be subject to merging errors
if the relative poses between depth maps are not known exactly.
the 3-D data may also have to be resampled before merging, which adds
additional complexity and potential sources of errors.
This paper provides a means of directly extracting
3-D data covering a very wide field of view, thus by-passing the
need for numerous depth map merging.
In our work, cylindrical images are first
sequences of images taken while the camera is rotated 360$^\circ$
about a vertical axis.
By taking such image panoramas at different camera locations, we
can recover 3-D data of the scene
using a set of simple techniques:
an 8-point structure from motion algorithm, and multibaseline stereo.
We also investigate the effect of median filtering on
the recovered 3-D point distributions, and show the results of our approach
applied to both synthetic and real scenes.