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Title: Neural Visibility Field for Uncertainty-Driven Active Mapping
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.  more » « less
Award ID(s):
2101250
PAR ID:
10639391
Author(s) / Creator(s):
 ;  ;  ;  ;  ;  
Publisher / Repository:
IEEE
Date Published:
Page Range / eLocation ID:
18122 to 18132
Format(s):
Medium: X
Location:
Seattle, WA
Sponsoring Org:
National Science Foundation
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