SparseSat-NeRF: Dense Depth Supervised Neural Radiance Fields for Sparse Satellite Images
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
Code (1)
Tasks
Earth ObservationNeRFStereo MatchingSimilar Papers 제목 키워드 기반
StructNeRF: Neural Radiance Fields for Indoor Scenes with Structural Hints
Neural Radiance Fields (NeRF) achieve photo-realistic view synthesis with densely captured input images. However, the geometry of NeRF is extremely under-constrained given sparse views, resulting in significant degradati…
Depth EstimationNeRFNovel View SynthesisNeRF-Supervision: Learning Dense Object Descriptors from Neural Radiance Fields
Thin, reflective objects such as forks and whisks are common in our daily lives, but they are particularly challenging for robot perception because it is hard to reconstruct them using commodity RGB-D cameras or multi-vi…
NeRFDense Depth Priors for Neural Radiance Fields from Sparse Input Views
Neural radiance fields (NeRF) encode a scene into a neural representation that enables photo-realistic rendering of novel views. However, a successful reconstruction from RGB images requires a large number of input views…
Depth CompletionNeRFNovel View SynthesisImproving Neural Radiance Fields with Depth-aware Optimization for Novel View Synthesis
With dense inputs, Neural Radiance Fields (NeRF) is able to render photo-realistic novel views under static conditions. Although the synthesis quality is excellent, existing NeRF-based methods fail to obtain moderate thr…
Depth EstimationNeRFNovel View SynthesisEnhancing Neural Radiance Fields with Depth and Normal Completion Priors from Sparse Views
Neural Radiance Fields (NeRF) are an advanced technology that creates highly realistic images by learning about scenes through a neural network model. However, NeRF often encounters issues when there are not enough image…
NeRFPatch Matching