Neural Visibility Field for Uncertainty-Driven Active Mapping

Autor:	Xue, Shangjie, Dill, Jesse, Mathur, Pranay, Dellaert, Frank, Tsiotras, Panagiotis, Xu, Danfei
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Robotics
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted to CVPR 2024. More details can be found at https://sites.google.com/view/nvf-cvpr24/
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2406.06948 Zobrazit plný text záznamu View this record from Arxiv