  This paper studies the problem of how to choose good viewpoints for taking
photographs of architectures. We achieve this by learning from professional
photographs of world famous landmarks that are available on the Internet.
Unlike previous efforts devoted to photo quality assessment which mainly rely
on 2D image features, we show in this paper combining 2D image features
extracted from images with 3D geometric features computed on the 3D models can
result in more reliable evaluation of viewpoint quality. Specifically, we
collect a set of photographs for each of 15 world famous architectures as well
as their 3D models from the Internet. Viewpoint recovery for images is carried
out through an image-model registration process, after which a newly proposed
viewpoint clustering strategy is exploited to validate users' viewpoint
preferences when photographing landmarks. Finally, we extract a number of 2D
and 3D features for each image based on multiple visual and geometric cues and
perform viewpoint recommendation by learning from both 2D and 3D features using
a specifically designed SVM-2K multi-view learner, achieving superior
performance over using solely 2D or 3D features. We show the effectiveness of
the proposed approach through extensive experiments. The experiments also
demonstrate that our system can be used to recommend viewpoints for rendering
textured 3D models of buildings for the use of architectural design, in
addition to viewpoint evaluation of photographs and recommendation of
viewpoints for photographing architectures in practice.
