  Implicit neural representations have demonstrated significant promise for 3D
scene reconstruction. Recent works have extended their applications to
autonomous implicit reconstruction through the Next Best View (NBV) based
method. However, the NBV method cannot guarantee complete scene coverage and
often necessitates extensive viewpoint sampling, particularly in complex
scenes. In the paper, we propose to 1) incorporate frontier-based exploration
tasks for global coverage with implicit surface uncertainty-based
reconstruction tasks to achieve high-quality reconstruction. and 2) introduce a
method to achieve implicit surface uncertainty using color uncertainty, which
reduces the time needed for view selection. Further with these two tasks, we
propose an adaptive strategy for switching modes in view path planning, to
reduce time and maintain superior reconstruction quality. Our method exhibits
the highest reconstruction quality among all planning methods and superior
planning efficiency in methods involving reconstruction tasks. We deploy our
method on a UAV and the results show that our method can plan multi-task views
and reconstruct a scene with high quality.
