论文标题
RESDEPTH：学习的残留立体声重建
ResDepth: Learned Residual Stereo Reconstruction
论文作者
论文摘要
我们提出了一个令人尴尬的简单但非常有效的方案，用于高质量的密集立体声重建：（i）使用您喜欢的立体声匹配器产生大致重建； （ii）用该近似模型重新吸收输入图像； （iii）最初的重建和扭曲的图像作为输入，训练深层网络，通过回归剩余校正来增强重建； （iv）如果需要，请务必通过新的，改进的重建。仅学习残留的策略极大地简化了学习问题。没有铃铛和哨声的标准UNET足以重建卫星图像中的窗户和屋顶子结构，甚至可以重建小的表面细节。我们还使用较少的信息研究了残留的重建，发现即使是单个图像也足以大大改善近似重建。我们的完整模型将最新立体声重建系统的平均绝对误差降低了> 50％，无论是在我们的卫星立体声目标域还是通过ETH3D基准的立体声对。
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.
