Optical sensor applications have become popular through digital transformation. Linking observed data to real-world locations and combining different image sensors is essential to make the applications practical and efficient. However, data preparation to try different sensor combinations requires high sensing and image processing expertise. To make data preparation easier for users unfamiliar with sensing and image processing, we have developed MultiBARF. This method replaces the co-registration and geometric calibration by synthesizing pairs of two different sensor images and depth images at assigned viewpoints. Our method extends Bundle Adjusting Neural Radiance Fields(BARF), a deep neural network-based novel view synthesis method, for the two imagers. Through experiments on visible light and thermographic images, we demonstrate that our method superimposes two color channels of those sensor images on NeRF.
View on arXiv@article{kurata2025_2503.15070,
title={ MultiBARF: Integrating Imagery of Different Wavelength Regions by Using Neural Radiance Fields },
author={ Kana Kurata and Hitoshi Niigaki and Xiaojun Wu and Ryuichi Tanida },
journal={arXiv preprint arXiv:2503.15070},
year={ 2025 }
}