  A high-quality 3D reconstruction of a scene from a collection of 2D images
can be achieved through offline/online mapping methods. In this paper, we
explore active mapping from the perspective of implicit representations, which
have recently produced compelling results in a variety of applications. One of
the most popular implicit representations - Neural Radiance Field (NeRF), first
demonstrated photorealistic rendering results using multi-layer perceptrons,
with promising offline 3D reconstruction as a by-product of the radiance field.
More recently, researchers also applied this implicit representation for online
reconstruction and localization (i.e. implicit SLAM systems). However, the
study on using implicit representation for active vision tasks is still very
limited. In this paper, we are particularly interested in applying the neural
radiance field for active mapping and planning problems, which are closely
coupled tasks in an active system. We, for the first time, present an RGB-only
active vision framework using radiance field representation for active 3D
reconstruction and planning in an online manner. Specifically, we formulate
this joint task as an iterative dual-stage optimization problem, where we
alternatively optimize for the radiance field representation and path planning.
Experimental results suggest that the proposed method achieves competitive
results compared to other offline methods and outperforms active reconstruction
methods using NeRFs.
