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.

本文提出神经显性场（NVF），这是一种用于应用于主动映射的神经辐射场（NeRF）的新型不确定性量化方法。通过使用贝叶斯网络将基于位置的场不确定性组合到基于光线的相机观测不确定性中，NVF自然地将更高的不确定性分配给未观察到的区域，帮助机器人选择最具信息量的下一个视点。大量的评估结果表明，NVF在不确定性量化和场景重建方面表现出色，优于现有方法。

基于不确定性驱动的主动建图的神经可见度场