  Implicit neural representations have shown compelling results in offline 3D
reconstruction and also recently demonstrated the potential for online SLAM
systems. However, applying them to autonomous 3D reconstruction, where a robot
is required to explore a scene and plan a view path for the reconstruction, has
not been studied. In this paper, we explore for the first time the possibility
of using implicit neural representations for autonomous 3D scene reconstruction
by addressing two key challenges: 1) seeking a criterion to measure the quality
of the candidate viewpoints for the view planning based on the new
representations, and 2) learning the criterion from data that can generalize to
different scenes instead of a hand-crafting one. To solve the challenges,
firstly, a proxy of Peak Signal-to-Noise Ratio (PSNR) is proposed to quantify a
viewpoint quality; secondly, the proxy is optimized jointly with the parameters
of an implicit neural network for the scene. With the proposed view quality
criterion from neural networks (termed as Neural Uncertainty), we can then
apply implicit representations to autonomous 3D reconstruction. Our method
demonstrates significant improvements on various metrics for the rendered image
quality and the geometry quality of the reconstructed 3D models when compared
with variants using TSDF or reconstruction without view planning. Project
webpage https://kingteeloki-ran.github.io/NeurAR/
