Digital surface model generation using traditional multi-view stereo matching (MVS) performs poorly over non-Lambertian surfaces, with asynchronous acquisitions, or at discontinuities. Neural radiance fields (NeRF) offer a new paradigm for reconstructing surface geometries using continuous volumetric representation. NeRF is self-supervised, does not require ground truth geometry for training, and provides an elegant way to include in its representation physical parameters about the scene, thus potentially remedying the challenging scenarios where MVS fails. However, NeRF and its variants require many views to produce convincing scene's geometries which in earth observation satellite imaging is rare. In this paper we present SparseSat-NeRF (SpS-NeRF) - an extension of Sat-NeRF adapted to sparse satellite views. SpS-NeRF employs dense depth supervision guided by crosscorrelation similarity metric provided by traditional semi-global MVS matching. We demonstrate the effectiveness of our approach on stereo and tri-stereo Pleiades 1B/WorldView-3 images, and compare against NeRF and Sat-NeRF. The code is available at https://github.com/LulinZhang/SpS-NeRF

翻译：利用传统多视图立体匹配（MVS）生成数字表面模型，在非朗伯表面、异步采集或间断区域表现不佳。神经辐射场（NeRF）通过连续体素表示为重建表面几何提供了新范式。NeRF采用自监督学习，无需训练所需的地面真实几何数据，并能优雅地将场景物理参数融入其表示中，从而有望改善MVS失效的困难场景。然而，NeRF及其变体需要大量视图才能生成可信的场景几何，这在地球观测卫星成像中十分罕见。本文提出SparseSat-NeRF（SpS-NeRF）——一种针对稀疏卫星视图优化的Sat-NeRF扩展方法。SpS-NeRF采用由传统半全局MVS匹配提供的交叉相关相似性度量进行稠密深度监督。我们在立体及三视角Pleiades 1B/WorldView-3图像上验证了该方法有效性，并与NeRF及Sat-NeRF进行对比。代码开源地址：https://github.com/LulinZhang/SpS-NeRF