  We present NARUTO, a neural active reconstruction system that combines a
hybrid neural representation with uncertainty learning, enabling high-fidelity
surface reconstruction. Our approach leverages a multi-resolution hash-grid as
the mapping backbone, chosen for its exceptional convergence speed and capacity
to capture high-frequency local features.The centerpiece of our work is the
incorporation of an uncertainty learning module that dynamically quantifies
reconstruction uncertainty while actively reconstructing the environment. By
harnessing learned uncertainty, we propose a novel uncertainty aggregation
strategy for goal searching and efficient path planning. Our system
autonomously explores by targeting uncertain observations and reconstructs
environments with remarkable completeness and fidelity. We also demonstrate the
utility of this uncertainty-aware approach by enhancing SOTA neural SLAM
systems through an active ray sampling strategy. Extensive evaluations of
NARUTO in various environments, using an indoor scene simulator, confirm its
superior performance and state-of-the-art status in active reconstruction, as
evidenced by its impressive results on benchmark datasets like Replica and
MP3D.
