depth and that of DORN. Fig. \[fig:pcd cmp\] demonstrate that the overall quality of ours outperforms theirs significantly. Although our predicted depth is only slightly better than theirs on evaluation metrics, the reconstructed wall (see the 2nd row in  \[fig:pcd cmp\]) of ours is much flatter and has fewer errors. The shape of the bed is more similar to the ground truth. From the bird view, it is hard to recognize the bed shape of their results. The point cloud in Fig. \[fig:pcd normal dorn ours\] also leads to a similar conclusion.

![Comparison of reconstructed point clouds from estimated depth maps between DORN [@fu2018deep] and ours. We can see that our point cloud results contain less noise and are closer to groud-truth than that of DORN.[]{data-label="fig:pcd cmp"}](./Fig/pcd_cmp){width="45.00000%"}

**Surface Normal.** Lastly, we compare the calculated surface normal with previous state-of-the-art methods and demonstrate the quantitative results in Table \[table:surface normals cmp\]. The ground truth is obtained as described in [@eigen2015predicting]. We first compare our geometrically calculated results with DCNN-based optimization methods. Although we do not optimize a sub-model to achieve the surface normal, our results can outperform most of the previous methods and even are the best on $\boldsymbol{30}\degree$ metric.

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