we can obtain a large number of such constraints, encoding the global 3D geometric. Second, *by converting estimated depth maps from images to 3D point cloud representations it opens many possibilities of incorporating algorithms for 3D point cloud processing to 2D images and 2.5D depth processing.* Here we show one instance of such possibilies.

By combining the high-order geometric supervision and the pixel-wise depth supervision, our network can predict not only an accurate depth map but also the high-quality 3D point cloud, subsequently other geometry information such as the surface normal. It is worth noting that we do not use a new model or introduce network branches for estimating the surface normal. Instead it is computed directly from the reconstructed point cloud. The second row of Fig. \[fig:pcd normal dorn ours\] demonstrates an example of our results. By contrast, although the previously state-of-the-art method [@Hu2018RevisitingSI] predicts the depth with low errors, the reconstructed point cloud is far away from the original shape (see, e.g., left part of ‘sofa’). The surface normal also contains many errors. We are probably the first to achieve high-quality monocular depth and surface normal prediction with a single network.

Experimental results on NYUD-v2 [@silberman2012indoor] and KITTI 