Implicit representations such as Neural Radiance Fields (NeRF) have been
shown to be very effective at novel view synthesis. However, these models
typically require manual and careful human data collection for training. In
this paper, we present AutoNeRF, a method to collect data required to train
NeRFs using autonomous embodied agents. Our method allows an agent to explore
an unseen environment efficiently and use the experience to build an implicit
map representation autonomously. We compare the impact of different exploration
strategies including handcrafted frontier-based exploration and modular
approaches composed of trained high-level planners and classical low-level path
followers. We train these models with different reward functions tailored to
this problem and evaluate the quality of the learned representations on four
different downstream tasks: classical viewpoint rendering, map reconstruction,
planning, and pose refinement. Empirical results show that NeRFs can be trained
on actively collected data using just a single episode of experience in an
unseen environment, and can be used for several downstream robotic tasks, and
that modular trained exploration models significantly outperform the classical
baselines.

该论文介绍了 AutoNeRF，一种使用自主实体代理收集数据以训练 NeRF 的方法，研究比较了不同的探索策略，经验证明可以使用主动收集的数据来训练 NeRF，并且可以用于下游机器人任务，以及模块化训练探索模型显著优于经典基线。