Towards Wiki-based Dense City Modeling - Institute for Computer ...

(a)
Figure 2. (a) Ten sample images from a collection of 105 facade images. (b) Side view of the camera poses and 9097 triangulated feature
points.
(b)
measurements is available, we use pose estimation for verification.
The calibrated settings enable us to use the Five-
Point algorithm [11] for image-to-image matches and the
Three-Point algorithm [6] for 3D to 2D correspondences
as minimal hypothesis generators. Images are accepted as
neighbors if a quality criterion is satisfied: we generate a
number of N hypothesis in prior and then check if the number
of outliers does not exceed the precomputed threshold.
Since it has often been observed (e.g. in [20]) that the
RANSAC stopping criterion is sometimes optimistic, we
use a rather conservative confidence factor p of 1−10 −4 and
require a minimal number of 20 inliers. If an image passes
the post-verification procedure, the neighbors of this image
are also matched with the query image. Note, that this strategy
is highly efficient for our purpose, since frequently the
first ranked image by the vocabulary tree is already a candidate
match satisfying the geometry constraints.
Unlike for the vocabulary tree scoring, where the whole
set of SIFT descriptors is utilized, we limit the number of
features for exhaustive matching. In general we take the
1500 best features according to their DoG magnitude response
for matching, only. Therefore, searching for correspondences
is sufficiently fast (see Section 5 for detailed
timings).
3.3. Upgrading the View Network
Each time a new image is added to the view network,
its neighbors are computed and the reconstruction process
is started. By reconstruction we mean structure and motion
computation, namely the estimation of the camera orientations
and the sparse 3D structure of the matched features.
Since images may be taken from different locations, we
do not expect to obtain a single coherent reconstruction, but
a forest of multiple reconstructions. We require that a reconstruction
consists of at least three images (view triple)
and 20 common triangulated points. In general four different
cases can occur if a new image is processed:
1. The view can be robustly registered with exactly one
already present reconstruction.
2. The image can be aligned with multiple reconstructions.
3. The current view cannot be (robustly) aligned with an
existing reconstruction, but forms a good view triple
with two other already present views.
4. The geometric relation of this image with any of the
known views cannot be established, and structure and
motion determination is postponed until a new suitable
view is inserted.
In the first case the position of the current view can be computed
immediately by robust absolute pose estimation, since
2D to 3D points correspondences are known. Thereafter,
the camera parameters are optimized by iterative refinement
and new correspondences are triangulated to 3D points.
In the second case, where the current image takes part of
two or more different reconstructions, the reconstructions
are merged. We determine robustly a 3D to 3D similarity
transform for the registration of the two corresponding 3D
points. This process is then followed by Euclidean bundle
adjustment, where the sparse geometry and the camera parameters
are optimized.
After updating the reconstructions in this first two cases,
the epipolar neighbors of the newly inserted view are traversed
and checked, whether the updated 3D structure now
allows determination of their poses.
In the third scenario the neighbors of the current image
are estimated as described previously and a new reconstruction
is initialized from a well-conditioned view triple. The
view triple should provide a good triangulation angle and at