Martin Cadik
(Brno University of Technology, Czech Republic)
Abstract:
We present a system for the annotation and augmentation of mountain
photographs. The key issue resides in the registration of a given
photograph with a 3D geo-referenced terrain model. Typical outdoor images
contain little structural information, particularly mountain scenes whose
aspect changes drastically across seasons and varying weather conditions.
Existing approaches usually fail on such difficult scenarios. To avoid
the burden of manual registration, we propose a novel automatic
technique. Given only a viewpoint and FOV estimates, the technique is
able to automatically derive the pose of the camera relative to the
geometric terrain model. We make use of silhouette edges, which are one
of the most reliable features that can be detected in the targeted
situations. Using an edge detection algorithm, our technique then
searches for the best match with silhouette edges rendered with the
synthetic model. We develop a robust matching metric allowing us to cope
with noise inevitably present among detected edges (e.g. due to clouds,
snow, rocks, forests, or any phenomenon not encoded in the digital
model). Once registered against the model, photographs can easily be
augmented with annotations (e.g. topographic data, peak names, paths),
which would otherwise imply a tedious fusion process. We further
illustrate various other applications, such as 3D model-assisted image
enhancement, or, inversely, texturing of digital models.
Look at
example
results if interested.