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Novel view synthesis of satellite images holds a wide range of practicalapplications. While recent advances in the Neural Radiance Field havepredominantly targeted pin-hole cameras, and models for satellite cameras oftendemand sufficient input views. This paper presents rpcPRF, a Multiplane Images(MPI) based Planar neural Radiance Field for Rational Polynomial Camera (RPC).Unlike coordinate-based neural radiance fields in need of sufficient views ofone scene, our model is applicable to single or few inputs and performs well onimages from unseen scenes. To enable generalization across scenes, we proposeto use reprojection supervision to induce the predicted MPI to learn thecorrect geometry between the 3D coordinates and the images. Moreover, we removethe stringent requirement of dense depth supervision from deepmultiview-stereo-based methods by introducing rendering techniques of radiancefields. rpcPRF combines the superiority of implicit representations and theadvantages of the RPC model, to capture the continuous altitude space whilelearning the 3D structure. Given an RGB image and its corresponding RPC, theend-to-end model learns to synthesize the novel view with a new RPC andreconstruct the altitude of the scene. When multiple views are provided asinputs, rpcPRF exerts extra supervision provided by the extra views. On the TLCdataset from ZY-3, and the SatMVS3D dataset with urban scenes from WV-3, rpcPRFoutperforms state-of-the-art nerf-based methods by a significant margin interms of image fidelity, reconstruction accuracy, and efficiency, for bothsingle-view and multiview task.

rpcPRF: Generalizable MPI Neural Radiance Field for Satellite Camera