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.

翻译：我们提出NARUTO系统，一种结合混合神经表征与不确定性学习的神经主动重建方法，可实现高保真表面重建。该方法采用多分辨率哈希网格作为建图主干，该结构因具有卓越的收敛速度及捕获高频局部特征的能力而被选用。本研究的核心在于集成不确定性学习模块，该模块在主动重建环境的同时动态量化重建不确定性。通过利用学习到的不确定性，我们提出一种新颖的不确定性聚合策略，用于目标搜索与高效路径规划。该系统通过定位不确定观测实现自主探索，并以卓越的完整性与保真度重建环境。此外，我们通过主动光线采样策略增强现有最优神经SLAM系统，证明了该不确定性感知方法的实用性。基于室内场景模拟器在多种环境下的全面评估，NARUTO在主动重建任务中展现出优越性能与最优水平，其在Replica和MP3D等基准数据集上的显著结果亦佐证了这一点。