more stable geometric constraint from a global perspective to take long-range relations into account for predicting depth, termed *virtual normal*. A few previous methods already made use of 3D geometric information in depth estimation, almost all of which focus on using surface normal. *We instead reconstruct the 3D point cloud from the estimated depth map explicitly.* In other words, we generate the 3D scene by lifting each RGB pixel in the 2D image to its corresponding 3D coordinate with the estimated depth map. This 3D point cloud serves as an intermediate representation. With the reconstructed point cloud, we can exploit many kinds of 3D geometry information, not limited to the surface normal. Here we consider the long-range dependency in the 3D space by randomly sampling three non-colinear points with the large distance to form a *virtual plane*, of which the normal vector is the proposed *virtual normal* (VN). The direction divergence between ground-truth and predicted VN can serve as a high-order 3D geometry loss. Owing to the long-range sampling of points, the adverse impact caused by noises in depth measurement is much alleviated compared to the computation of the surface normal, making VN significantly more accurate. Moreover, with randomly sampling 