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。