We introduce a novel approach for adapting deep stereo networks in a
collaborative manner. By building over principles of federated learning, we
develop a distributed framework allowing for demanding the optimization process
to a number of clients deployed in different environments. This makes it
possible, for a deep stereo network running on resourced-constrained devices,
to capitalize on the adaptation process carried out by other instances of the
same architecture, and thus improve its accuracy in challenging environments
even when it cannot carry out adaptation on its own. Experimental results show
how federated adaptation performs equivalently to on-device adaptation, and
even better when dealing with challenging environments.

我们提出了一种新颖的方法，通过在协同学习的基础上构建分布式框架，使得深度立体网络能够从其他相同架构的实例进行自适应过程，从而在不具备自适应能力的资源受限设备上，在具有挑战性的环境中提高准确性。实验结果表明，分布式自适应与设备上自适应相当甚至在处理挑战性环境时更好。

深度立体联邦在线适应

Federated Online Adaptation for Deep Stereo

We introduce a novel framework for training deep stereo networks effortlessly
and without any ground-truth. By leveraging state-of-the-art neural rendering
solutions, we generate stereo training data from image sequences collected with
a single handheld camera. On top of them, a NeRF-supervised training procedure
is carried out, from which we exploit rendered stereo triplets to compensate
for occlusions and depth maps as proxy labels. This results in stereo networks
capable of predicting sharp and detailed disparity maps. Experimental results
show that models trained under this regime yield a 30-40% improvement over
existing self-supervised methods on the challenging Middlebury dataset, filling
the gap to supervised models and, most times, outperforming them at zero-shot
generalization.

使用神经渲染技术构建的数据集来训练深度立体网络，使其能够预测锐利且详细的视差图，并在 Middlebury 数据集上优于现有的自监督方法。