[@geiger2013vision] datasets demonstrate state-of-the-art performance of our method. Besides, when training with the lightweight backbone, MobileNetV2 [@sandler2018mobilenetv2], our framework provides a better trade-off between network parameters and accuracy. Our method outperforms other state-of-the-art real-time systems by up to $29$% with a comparable number of network parameters. Furthermore, from the reconstructed point cloud, we directly calculate the surface normal, with a precision being on par with that of specific DCNN based surface normal estimation methods.

In summary, our main contributions of this work are as follow.

-   We demonstrate the effectiveness of enforcing a high-order geometric constraint in the 3D space for the depth prediction task. Such global geometry information is instantiated with a simple yet effective concept termed *virtual normal* (VN). By enforcing a loss defined on VNs, we demonstrate the importance of 3D geometry information in depth estimation, and design a simple loss to exploit it.

-   Our method can reconstruct high-quality 3D scene point clouds, from which other 3D geometry features can be calculated, such as the surface normal. In essence, we show that for depth estimation, one should not consider the information represented by depth only. Instead, converting depth into 3D point clouds and exploiting 3D geometry 