This paper presents Neural Visibility Field (NVF), a novel uncertainty quantification method for Neural Radiance Fields (NeRF) applied to active mapping. Our key insight is that regions not visible in the training views lead to inherently unreliable color predictions by NeRF at this region, resulting in increased uncertainty in the synthesized views. To address this, we propose to use Bayesian Networks to composite position-based field uncertainty into ray-based uncertainty in camera observations. Consequently, NVF naturally assigns higher uncertainty to unobserved regions, aiding robots to select the most informative next viewpoints. Extensive evaluations show that NVF excels not only in uncertainty quantification but also in scene reconstruction for active mapping, outperforming existing methods.

翻译：本文提出神经可见性场（NVF），一种应用于主动建图的新型神经辐射场（NeRF）不确定性量化方法。我们的核心见解是：训练视角中不可见的区域会导致NeRF在该区域的色彩预测本质上不可靠，从而在合成视图中产生更高的不确定性。为解决此问题，我们提出使用贝叶斯网络将基于位置的场不确定性合成为相机观测中基于射线的不确定性。因此，NVF能自然地赋予未观测区域更高的不确定性，辅助机器人选择信息量最大的下一视点。大量实验评估表明，NVF不仅在不确定性量化方面表现优异，在主动建图的场景重建任务中也超越了现有方法。