Monocular depth estimation is scale-ambiguous, and thus requires scale
supervision to produce metric predictions. Even so, the resulting models will
be geometry-specific, with learned scales that cannot be directly transferred
across domains. Because of that, recent works focus instead on relative depth,
eschewing scale in favor of improved up-to-scale zero-shot transfer. In this
work we introduce ZeroDepth, a novel monocular depth estimation framework
capable of predicting metric scale for arbitrary test images from different
domains and camera parameters. This is achieved by (i) the use of input-level
geometric embeddings that enable the network to learn a scale prior over
objects; and (ii) decoupling the encoder and decoder stages, via a variational
latent representation that is conditioned on single frame information. We
evaluated ZeroDepth targeting both outdoor (KITTI, DDAD, nuScenes) and indoor
(NYUv2) benchmarks, and achieved a new state-of-the-art in both settings using
the same pre-trained model, outperforming methods that train on in-domain data
and require test-time scaling to produce metric estimates.

本文提出了一种称为 ZeroDepth 的单目深度估计框架，采用输入级几何嵌入和变分潜在表示，并将编码器和解码器阶段解耦合，可以在不同域和相机参数的情况下预测任意测试图像的尺度。在户内（NYUv2）和户外（KITTI，DDAD，nuScenes）基准测试中，使用相同的预训练模型取得了最新的最佳成果，优于基于域内数据训练和需要测试时缩放以产生度量估计的方法。

朝着零样本比例感知单目深度估计的方向

Towards Zero-Shot Scale-Aware Monocular Depth Estimation

We present a method for depth estimation with monocular images, which can
predict high-quality depth on diverse scenes up to an affine transformation,
thus preserving accurate shapes of a scene. Previous methods that predict
metric depth often work well only for a specific scene. In contrast, learning
relative depth (information of being closer or further) can enjoy better
generalization, with the price of failing to recover the accurate geometric
shape of the scene. In this work, we propose a dataset and methods to tackle
this dilemma, aiming to predict accurate depth up to an affine transformation
with good generalization to diverse scenes. First we construct a large-scale
and diverse dataset, termed Diverse Scene Depth dataset (DiverseDepth), which
has a broad range of scenes and foreground contents. Compared with previous
learning objectives, i.e., learning metric depth or relative depth, we propose
to learn the affine-invariant depth using our diverse dataset to ensure both
generalization and high-quality geometric shapes of scenes. Furthermore, in
order to train the model on the complex dataset effectively, we propose a
multi-curriculum learning method. Experiments show that our method outperforms
previous methods on 8 datasets by a large margin with the zero-shot test
setting, demonstrating the excellent generalization capacity of the learned
model to diverse scenes. The reconstructed point clouds with the predicted
depth show that our method can recover high-quality 3D shapes. Code and dataset
are available at: this https URL

本文提供了一种使用单目图像进行深度估计的方法，提供了一个大规模且多样化的数据集 Diverse Scene Depth 进行训练，学习到了不受仿射变换影响的深度信息，并通过多分支课程训练实现了对复杂场景的高质量深度恢复。