Reconstructing accurate 3D scenes from images is a long-standing vision task.
Due to the ill-posedness of the single-image reconstruction problem, most
well-established methods are built upon multi-view geometry. State-of-the-art
(SOTA) monocular metric depth estimation methods can only handle a single
camera model and are unable to perform mixed-data training due to the metric
ambiguity. Meanwhile, SOTA monocular methods trained on large mixed datasets
achieve zero-shot generalization by learning affine-invariant depths, which
cannot recover real-world metrics. In this work, we show that the key to a
zero-shot single-view metric depth model lies in the combination of large-scale
data training and resolving the metric ambiguity from various camera models. We
propose a canonical camera space transformation module, which explicitly
addresses the ambiguity problems and can be effortlessly plugged into existing
monocular models. Equipped with our module, monocular models can be stably
trained with over 8 million images with thousands of camera models, resulting
in zero-shot generalization to in-the-wild images with unseen camera settings.
Experiments demonstrate SOTA performance of our method on 7 zero-shot
benchmarks. Notably, our method won the championship in the 2nd Monocular Depth
Estimation Challenge. Our method enables the accurate recovery of metric 3D
structures on randomly collected internet images, paving the way for plausible
single-image metrology. The potential benefits extend to downstream tasks,
which can be significantly improved by simply plugging in our model. For
example, our model relieves the scale drift issues of monocular-SLAM (Fig. 1),
leading to high-quality metric scale dense mapping. The code is available at
this https URL

基于大规模数据训练和解决来自不同摄像机模型的度量歧义，实现了在野外图像中无需训练即可广义泛化的单视图度量深度模型，并在 7 个零样本基准测试上展现了领先表现。

Metric3D：基于单张图片的零样本度量三维预测

Metric3D: Towards Zero-shot Metric 3D Prediction from A Single Image

For autonomous vehicles, driving safely is highly dependent on the capability
to correctly perceive the environment in 3D space, hence the task of 3D object
detection represents a fundamental aspect of perception. While 3D sensors
deliver accurate metric perception, monocular approaches enjoy cost and
availability advantages that are valuable in a wide range of applications.
Unfortunately, training monocular methods requires a vast amount of annotated
data. Interestingly, self-supervised approaches have recently been successfully
applied to ease the training process and unlock access to widely available
unlabelled data. While related research leverages different priors including
LIDAR scans and stereo images, such priors again limit usability. Therefore, in
this work, we propose a novel approach to self-supervise 3D object detection
purely from RGB sequences alone, leveraging multi-view constraints and weak
labels. Our experiments on KITTI 3D dataset demonstrate performance on par with
state-of-the-art self-supervised methods using LIDAR scans or stereo images.

提出了一种基于多视角和弱标签的纯 RGB 序列的自监督 3D 物体检测方法，其在 KITTI 3D 数据集上的性能与使用激光雷达或立体图像的最先进的自监督方法相当。