We propose an embarrassingly simple but very effective scheme for
high-quality dense stereo reconstruction: (i) generate an approximate
reconstruction with your favourite stereo matcher; (ii) rewarp the input images
with that approximate model; (iii) with the initial reconstruction and the
warped images as input, train a deep network to enhance the reconstruction by
regressing a residual correction; and (iv) if desired, iterate the refinement
with the new, improved reconstruction. The strategy to only learn the residual
greatly simplifies the learning problem. A standard Unet without bells and
whistles is enough to reconstruct even small surface details, like dormers and
roof substructures in satellite images. We also investigate residual
reconstruction with less information and find that even a single image is
enough to greatly improve an approximate reconstruction. Our full model reduces
the mean absolute error of state-of-the-art stereo reconstruction systems by
>50%, both in our target domain of satellite stereo and on stereo pairs from
the ETH3D benchmark.

使用深度学习的残差学习方法来提高立体重建质量，并可用于卫星图像等领域。在 ETH3D 基准和卫星图像领域，平均绝对误差降低了 50% 以上。