FusionRF: High-Fidelity Satellite Neural Radiance Fields from Multispectral and Panchromatic Acquisitions
<|reference_start|>FusionRF: High-Fidelity Satellite Neural Radiance Fields from Multispectral and Panchromatic Acquisitions: We introduce FusionRF, a novel neural rendering terrain reconstruction method from optically unprocessed satellite imagery. While previous methods depend on external pansharpening methods to fuse low resolution multispectral imagery and high resolution panchromatic imagery, FusionRF directly performs reconstruction based on optically unprocessed acquisitions with no prior knowledge. This is accomplished through the addition of a sharpening kernel which models the resolution loss in multispectral images. Additionally, novel modal embeddings allow the model to perform image fusion as a bottleneck to novel view synthesis. We evaluate our method on multispectral and panchromatic satellite images from the WorldView-3 satellite in various locations, and FusionRF outperforms previous State-of-The-Art methods in depth reconstruction on unprocessed imagery, renders sharp training and novel views, and retains multi-spectral information.<|reference_end|>
@article{sprintson2024fusionrf:,
    title={FusionRF: High-Fidelity Satellite Neural Radiance Fields from
  Multispectral and Panchromatic Acquisitions},
    author={Michael Sprintson, Rama Chellappa, and Cheng Peng},
    journal={arXiv preprint arXiv:2409.15132},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.15132},
    primaryClass={cs.CV eess.IV}
}