  High-quality observations of the real world are crucial for a variety of
applications, including producing 3D printed replicas of small-scale scenes and
conducting inspections of large-scale infrastructure. These 3D observations are
commonly obtained by combining multiple sensor measurements from different
views. Guiding the selection of suitable views is known as the NBV planning
problem.
  Most NBV approaches reason about measurements using rigid data structures
(e.g., surface meshes or voxel grids). This simplifies next best view selection
but can be computationally expensive, reduces real-world fidelity, and couples
the selection of a next best view with the final data processing.
  This paper presents the Surface Edge Explorer, a NBV approach that selects
new observations directly from previous sensor measurements without requiring
rigid data structures. SEE uses measurement density to propose next best views
that increase coverage of insufficiently observed surfaces while avoiding
potential occlusions. Statistical results from simulated experiments show that
SEE can attain similar or better surface coverage with less observation time
and travel distance than evaluated volumetric approaches on both small- and
large-scale scenes. Real-world experiments demonstrate SEE autonomously
observing a deer statue using a 3D sensor affixed to a robotic arm.
