  Current stereo-vision pipelines produce high accuracy 3D reconstruction when
using multiple pairs or triplets of satellite images. However, these pipelines
are sensitive to the changes between images that can occur as a result of
multi-date acquisitions. Such variations are mainly due to variable shadows,
reflexions and transient objects (cars, vegetation). To take such changes into
account, Neural Radiance Fields (NeRF) have recently been applied to multi-date
satellite imagery. However, Neural methods are very compute-intensive, taking
dozens of hours to learn, compared with minutes for standard stereo-vision
pipelines. Following the ideas of Instant Neural Graphics Primitives we propose
to use an efficient sampling strategy and multi-resolution hash encoding to
accelerate the learning. Our model, Satellite Neural Graphics Primitives
(SAT-NGP) decreases the learning time to 15 minutes while maintaining the
quality of the 3D reconstruction.
