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神经辐射场（NeRF）不确定性量化新方法。核心洞察在于：训练视角中不可见的区域会导致NeRF在该区域的颜色预测天然不可靠，从而增加合成视角的不确定性。针对此问题，我们提出使用贝叶斯网络将基于位置的不确定性场组合为相机观测中基于射线的场不确定性。由此，NVF自然地赋予未观测区域更高不确定性，帮助机器人选择信息量最大的下一视角。广泛评估表明，NVF不仅在不稳定性量化上表现卓越，在主动建图的场景重建中也优于现有方法。