We present a stereo-matching method for depth estimation from high-resolution
images using visual hulls as priors, and a memory-efficient technique for the
correlation computation. Our method uses object masks extracted from
supplementary views of the scene to guide the disparity estimation, effectively
reducing the search space for matches. This approach is specifically tailored
to stereo rigs in volumetric capture systems, where an accurate depth plays a
key role in the downstream reconstruction task. To enable training and
regression at high resolutions targeted by recent systems, our approach extends
a sparse correlation computation into a hybrid sparse-dense scheme suitable for
application in leading recurrent network architectures. We evaluate the
performance-efficiency trade-off of our method compared to state-of-the-art
methods, and demonstrate the efficacy of the visual hull guidance. In addition,
we propose a training scheme for a further reduction of memory requirements
during optimization, facilitating training on high-resolution data.

我们提出了一种用于从高分辨率图像中估计深度的立体匹配方法，该方法利用视觉外壳作为先验，并使用一种内存高效的相关计算技术。

VHS：具有视觉外壳先验的高分辨率迭代立体匹配

VHS: High-Resolution Iterative Stereo Matching with Visual Hull Priors

In the field of remote sensing, the scarcity of stereo-matched and
particularly lack of accurate ground truth data often hinders the training of
deep neural networks. The use of synthetically generated images as an
alternative, alleviates this problem but suffers from the problem of domain
generalization. Unifying the capabilities of image-to-image translation and
stereo-matching presents an effective solution to address the issue of domain
generalization. Current methods involve combining two networks, an unpaired
image-to-image translation network and a stereo-matching network, while jointly
optimizing them. We propose an edge-aware GAN-based network that effectively
tackles both tasks simultaneously. We obtain edge maps of input images from the
Sobel operator and use it as an additional input to the encoder in the
generator to enforce geometric consistency during translation. We additionally
include a warping loss calculated from the translated images to maintain the
stereo consistency. We demonstrate that our model produces qualitatively and
quantitatively superior results than existing models, and its applicability
extends to diverse domains, including autonomous driving.

远程感知领域中，缺乏立体匹配以及准确地面真实数据制约了深度神经网络的训练；合成图像作为替代方案缓解了此问题，但面临域泛化问题；结合图像到图像翻译和立体匹配的能力提供了有效解决方案；通过将图像到图像翻译网络和立体匹配网络联合优化，我们提出一种基于边缘感知的生成对抗网络，同时有效解决了两个任务；通过 Sobel 算子获取输入图像的边缘图像，并将其作为编码器的额外输入，以在翻译过程中强制保持几何一致性；此外，通过计算翻译图像的变形损失来维持立体一致性；我们证明了我们的模型比现有模型产生了更好的定性和定量结果，并且其适用性扩展到包括自动驾驶在内的各种领域。

SyntStereo2Real：边缘感知生成对抗网络用于维持立体约束的遥感图像转换

SyntStereo2Real: Edge-Aware GAN for Remote Sensing Image-to-Image  Translation while Maintaining Stereo Constraint

In stereo-matching knowledge distillation methods of the self-supervised
monocular depth estimation, the stereo-matching network's knowledge is
distilled into a monocular depth network through pseudo-depth maps. In these
methods, the learning-based stereo-confidence network is generally utilized to
identify errors in the pseudo-depth maps to prevent transferring the errors.
However, the learning-based stereo-confidence networks should be trained with
ground truth (GT), which is not feasible in a self-supervised setting. In this
paper, we propose a method to identify and filter errors in the pseudo-depth
map using multiple disparity maps by checking their consistency without the
need for GT and a training process. Experimental results show that the proposed
method outperforms the previous methods and works well on various
configurations by filtering out erroneous areas where the stereo-matching is
vulnerable, especially such as textureless regions, occlusion boundaries, and
reflective surfaces.

本研究提出了一种基于多个视差图来检查一致性的方法，以识别并过滤伪深度图中的错误，无需真实值（GT）和训练过程，并通过消除立体匹配易受攻击的错误区域，如无纹理区域、遮挡边界和反射表面，取得了比以往方法更好的实验结果。