squared error (rms) , root mean squared log error (rms (log)) and the accuracy under threshold ($\delta_{i} < 1.25^{i}, i=1, 2, 3$).

Comparison with State-of-the-art
--------------------------------

In this section, we detail the comparison of our methods with state-of-the-art methods.

\[table:errors cmp on NYUD-V2\]

**NYUD-V2.** In this experiment, we compare with other state-of-the-art methods on the NYUD-V2 dataset. Table \[table:errors cmp on NYUD-V2\] demonstrates that our proposed method outperforms other state-of-the-art methods across all evaluation metrics significantly. Compare to DORN, we have improved the accuracy from $0.2\% $ to $18\%$ over all evaluation metrics that they report.

In addition to the quantitative comparison, we demonstrate some visual results between our method and the state-of-the-art DORN in Fig. \[fig:visual cmp\]. Clearly, the predicted depth by the proposed method is much more accurate. The plane of ours is much smoother and has fewer errors (see the wall regions colored with red in the 1st, 2nd, and 3rd row). Furthermore, the last row in Fig. \[fig:visual cmp\] manifests that our predicted depth is more accurate in the complicated scene. We have fewer errors in shelf and desk regions.

![Examples of predicted depth maps by our method and the state-of-the-art DORN on NYUD-V2. Color indicates 