Supervised deep networks are among the best methods for finding
correspondences in stereo image pairs. Like all supervised approaches, these
networks require ground truth data during training. However, collecting large
quantities of accurate dense correspondence data is very challenging. We
propose that it is unnecessary to have such a high reliance on ground truth
depths or even corresponding stereo pairs. Inspired by recent progress in
monocular depth estimation, we generate plausible disparity maps from single
images. In turn, we use those flawed disparity maps in a carefully designed
pipeline to generate stereo training pairs. Training in this manner makes it
possible to convert any collection of single RGB images into stereo training
data. This results in a significant reduction in human effort, with no need to
collect real depths or to hand-design synthetic data. We can consequently train
a stereo matching network from scratch on datasets like COCO, which were
previously hard to exploit for stereo. Through extensive experiments we show
that our approach outperforms stereo networks trained with standard synthetic
datasets, when evaluated on KITTI, ETH3D, and Middlebury.

该研究介绍了一种基于单目深度估计，使用错误匹配视差地图训练立体匹配网络的方法，无需真实深度或手动设计人工数据即可将任何 RGB 图像列表转换为立体训练数据，并在 KITTI、ETH3D 和 Middlebury 等数据集上表现出色。

从单张图片学习立体视觉

Learning Stereo from Single Images

Supervised deep networks have achieved promisingperformance on image
denoising, by learning image priors andnoise statistics on plenty pairs of
noisy and clean images. Unsupervised denoising networks are trained with only
noisy images. However, for an unseen corrupted image, both supervised
andunsupervised networks ignore either its particular image prior, the noise
statistics, or both. That is, the networks learned from external images
inherently suffer from a domain gap problem: the image priors and noise
statistics are very different between the training and test images. This
problem becomes more clear when dealing with the signal dependent realistic
noise. To circumvent this problem, in this work, we propose a novel
"Noisy-As-Clean" (NAC) strategy of training self-supervised denoising networks.
Specifically, the corrupted test image is directly taken as the "clean" target,
while the inputs are synthetic images consisted of this corrupted image and a
second and similar corruption. A simple but useful observation on our NAC is:
as long as the noise is weak, it is feasible to learn a self-supervised network
only with the corrupted image, approximating the optimal parameters of a
supervised network learned with pairs of noisy and clean images. Experiments on
synthetic and realistic noise removal demonstrate that, the DnCNN and ResNet
networks trained with our self-supervised NAC strategy achieve comparable or
better performance than the original ones and previous
supervised/unsupervised/self-supervised networks. The code is publicly
available at this https URL

本文提出了一种用于培训自我监督去噪网络的新策略，即 “Noisy-As-Clean”(NAC) 策略，该策略可绕过训练和测试图像之间存在的领域差距问题，并展示了该策略能够达到比原始网络更好的去噪性能。