psPRF:Pansharpening Planar Neural Radiance Field for Generalized 3D Reconstruction Satellite Imagery
<|reference_start|>psPRF:Pansharpening Planar Neural Radiance Field for Generalized 3D Reconstruction Satellite Imagery: Most current NeRF variants for satellites are designed for one specific scene and fall short of generalization to new geometry. Additionally, the RGB images require pan-sharpening as an independent preprocessing step. This paper introduces psPRF, a Planar Neural Radiance Field designed for paired low-resolution RGB (LR-RGB) and high-resolution panchromatic (HR-PAN) images from satellite sensors with Rational Polynomial Cameras (RPC). To capture the cross-modal prior from both of the LR-RGB and HR-PAN images, for the Unet-shaped architecture, we adapt the encoder with explicit spectral-to-spatial convolution (SSConv) to enhance the multimodal representation ability. To support the generalization ability of psRPF across scenes, we adopt projection loss to ensure strong geometry self-supervision. The proposed method is evaluated with the multi-scene WorldView-3 LR-RGB and HR-PAN pairs, and achieves state-of-the-art performance.<|reference_end|>
@article{zhang2024psprf:pansharpening,
    title={psPRF:Pansharpening Planar Neural Radiance Field for Generalized 3D
  Reconstruction Satellite Imagery},
    author={Tongtong Zhang, Yuanxiang Li},
    journal={arXiv preprint arXiv:2406.15707},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.15707},
    primaryClass={cs.CV}
}