With the proliferation of small aerial vehicles, acquiring close up aerial
imagery for high quality reconstruction of complex scenes is gaining
importance. We present an adaptive view planning method to collect such images
in an automated fashion. We start by sampling a small set of views to build a
coarse proxy to the scene. We then present (i)~a method that builds a view
manifold for view selection, and (ii) an algorithm to select a sparse set of
views. The vehicle then visits these viewpoints to cover the scene, and the
procedure is repeated until reconstruction quality converges or a desired level
of quality is achieved. The view manifold provides an effective
efficiency/quality compromise between using the entire 6 degree of freedom pose
space and using a single view hemisphere to select the views.
Our results show that, in contrast to existing "explore and exploit" methods
which collect only two sets of views, reconstruction quality can be drastically
improved by adding a third set. They also indicate that three rounds of data
collection is sufficient even for very complex scenes. We compare our algorithm
to existing methods in three challenging scenes. We require each algorithm to
select the same number of views. Our algorithm generates views which produce
the least reconstruction error
