Nowadays, robotics, AR, and 3D modeling applications attract considerable
attention to single-view depth estimation (SVDE) as it allows estimating scene
geometry from a single RGB image. Recent works have demonstrated that the
accuracy of an SVDE method hugely depends on the diversity and volume of the
training data. However, RGB-D datasets obtained via depth capturing or 3D
reconstruction are typically small, synthetic datasets are not photorealistic
enough, and all these datasets lack diversity. The large-scale and diverse data
can be sourced from stereo images or stereo videos from the web. Typically
being uncalibrated, stereo data provides disparities up to unknown shift
(geometrically incomplete data), so stereo-trained SVDE methods cannot recover
3D geometry. It was recently shown that the distorted point clouds obtained
with a stereo-trained SVDE method can be corrected with additional point cloud
modules (PCM) separately trained on the geometrically complete data. On the
contrary, we propose GP$^{2}$, General-Purpose and Geometry-Preserving training
scheme, and show that conventional SVDE models can learn correct shifts
themselves without any post-processing, benefiting from using stereo data even
in the geometry-preserving setting. Through experiments on different dataset
mixtures, we prove that GP$^{2}$-trained models outperform methods relying on
PCM in both accuracy and speed, and report the state-of-the-art results in the
general-purpose geometry-preserving SVDE. Moreover, we show that SVDE models
can learn to predict geometrically correct depth even when geometrically
complete data comprises the minor part of the training set.

提出 GP$^{2}$ 训练方案，利用立体图像数据训练单视图深度估计模型，实现了在保持几何结构的同时提高了模型的准确度和速度，同时证明该模型在使用完备数据集很少情况下也能够学习到准确的深度预测。