Image-based depth estimation has gained significant attention in recent
research on computer vision for autonomous vehicles in intelligent
transportation systems. This focus stems from its cost-effectiveness and wide
range of potential applications. Unlike binocular depth estimation methods that
require two fixed cameras, monocular depth estimation methods only rely on a
single camera, making them highly versatile. While state-of-the-art approaches
for this task leverage self-supervised learning of deep neural networks in
conjunction with tasks like pose estimation and semantic segmentation, none of
them have explored the combination of federated learning and self-supervision
to train models using unlabeled and private data captured by autonomous
vehicles. The utilization of federated learning offers notable benefits,
including enhanced privacy protection, reduced network consumption, and
improved resilience to connectivity issues. To address this gap, we propose
FedSCDepth, a novel method that combines federated learning and deep
self-supervision to enable the learning of monocular depth estimators with
comparable effectiveness and superior efficiency compared to the current
state-of-the-art methods. Our evaluation experiments conducted on Eigen's Split
of the KITTI dataset demonstrate that our proposed method achieves near
state-of-the-art performance, with a test loss below 0.13 and requiring, on
average, only 1.5k training steps and up to 0.415 GB of weight data transfer
per autonomous vehicle on each round.

提出了一种新的方法 FedSCDepth，结合联邦学习和深度自监督，能够以与当前最先进的方法相媲美且更高效的方式进行单目深度估计的学习。在 KITTI 数据集上进行的评估实验表明，所提出的方法在测试损失低于 0.13，平均仅需要 1.5k 个训练步骤，并且每轮每辆自动驾驶车辆的权重数据传输量最多为 0.415 GB，达到了近乎最新技术的性能水平。

用于自动驾驶的联邦无监督单目深度估计器的学习

Federated Self-Supervised Learning of Monocular Depth Estimators for  Autonomous Vehicles

3D object detection is an essential task in autonomous driving. Recent
techniques excel with highly accurate detection rates, provided the 3D input
data is obtained from precise but expensive LiDAR technology. Approaches based
on cheaper monocular or stereo imagery data have, until now, resulted in
drastically lower accuracies --- a gap that is commonly attributed to poor
image-based depth estimation. However, in this paper we argue that it is not
the quality of the data but its representation that accounts for the majority
of the difference. Taking the inner workings of convolutional neural networks
into consideration, we propose to convert image-based depth maps to
pseudo-LiDAR representations --- essentially mimicking the LiDAR signal. With
this representation we can apply different existing LiDAR-based detection
algorithms. On the popular KITTI benchmark, our approach achieves impressive
improvements over the existing state-of-the-art in image-based performance ---
raising the detection accuracy of objects within the 30m range from the
previous state-of-the-art of 22% to an unprecedented 74%. At the time of
submission our algorithm holds the highest entry on the KITTI 3D object
detection leaderboard for stereo-image-based approaches. Our code is publicly
available at this https URL

本文提出一种基于卷积神经网络将基于图像的深度图转换为假激光雷达（pseudo-LiDAR）表示的方法，从而使得使用便宜的单 ocular 或立体成像数据进行 3D 物体检测时的精度在 KITTI 数据集上大幅提高到了 22% 到 74%。