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.

翻译：隐式表征（如神经辐射场NeRF）在新视角合成任务中展现出卓越效果，但此类模型通常依赖人工精细采集的数据进行训练。本文提出AutoNeRF方法，通过自主具身智能体采集训练NeRF所需数据。该方法使智能体能够在未知环境中高效探索，并自主利用探索经验构建隐式地图表征。我们比较了不同探索策略的影响，包括基于手工设计的前沿探索方法，以及由训练的高层级规划器与经典低层级路径追踪器组成的模块化方法。针对该问题设计了不同奖励函数训练这些模型，并在四项下游任务中评估学习表征的质量：经典视角渲染、地图重建、路径规划及位姿优化。实验结果表明，NeRF仅需在未知环境中进行单次探索即可通过主动采集的数据完成训练，并能应用于多种机器人下游任务，同时模块化训练的探索模型显著优于经典基线方法。