  Implicit neural representations have shown promising potential for the 3D
scene reconstruction. Recent work applies it to autonomous 3D reconstruction by
learning information gain for view path planning. Effective as it is, the
computation of the information gain is expensive, and compared with that using
volumetric representations, collision checking using the implicit
representation for a 3D point is much slower. In the paper, we propose to 1)
leverage a neural network as an implicit function approximator for the
information gain field and 2) combine the implicit fine-grained representation
with coarse volumetric representations to improve efficiency. Further with the
improved efficiency, we propose a novel informative path planning based on a
graph-based planner. Our method demonstrates significant improvements in the
reconstruction quality and planning efficiency compared with autonomous
reconstructions with implicit and explicit representations. We deploy the
method on a real UAV and the results show that our method can plan informative
views and reconstruct a scene with high quality.
