Monocular depth estimation enables 3D perception from a single 2D image, thus
attracting much research attention for years. Almost all methods treat
foreground and background regions ("things and stuff") in an image equally.
However, not all pixels are equal. Depth of foreground objects plays a crucial
role in 3D object recognition and localization. To date how to boost the depth
prediction accuracy of foreground objects is rarely discussed. In this paper,
we first analyse the data distributions and interaction of foreground and
background, then propose the foreground-background separated monocular depth
estimation (ForeSeE) method, to estimate the foreground depth and background
depth using separate optimization objectives and depth decoders. Our method
significantly improves the depth estimation performance on foreground objects.
Applying ForeSeE to 3D object detection, we achieve 7.5 AP gains and set new
state-of-the-art results among other monocular methods. Code will be available
at: this https URL

本文提出了一种单目深度估计方法，通过前景 - 背景分离以及单独优化前景深度和背景深度的目标函数和深度解码器，极大地提高了前景目标深度的估计性能，并在应用于 3D 对象检测中各方面都达到了最先进的成果。

针对 3D 物体检测的任务感知单目深度估计

Task-Aware Monocular Depth Estimation for 3D Object Detection

Monocular depth prediction plays a crucial role in understanding 3D scene
geometry. Although recent methods have achieved impressive progress in
evaluation metrics such as the pixel-wise relative error, most methods neglect
the geometric constraints in the 3D space. In this work, we show the importance
of the high-order 3D geometric constraints for depth prediction. By designing a
loss term that enforces one simple type of geometric constraints, namely,
virtual normal directions determined by randomly sampled three points in the
reconstructed 3D space, we can considerably improve the depth prediction
accuracy. Significantly, the byproduct of this predicted depth being
sufficiently accurate is that we are now able to recover good 3D structures of
the scene such as the point cloud and surface normal directly from the depth,
eliminating the necessity of training new sub-models as was previously done.
Experiments on two benchmarks: NYU Depth-V2 and KITTI demonstrate the
effectiveness of our method and state-of-the-art performance.

本研究提出了一种基于高阶 3D 几何约束的深度预测模型，通过在重建的 3D 空间中随机采样三个点确定虚拟法向方向并设计相应的损失函数，能够显著提高深度预测的准确性，从而实现直接从深度图中恢复场景的 3D 结构（如点云和表面法向），进而消除了训练新子模型的必要性，并在 NYU Depth-V2 和 KITTI 等数据集上达到了最先进的性能水平。