The pre-trained ResNeXt-101 [@xie2017aggregated] $(32 \times 4d)$ model on ImageNet [@deng2009imagenet] is used as our backbone model. A polynomial decaying method with the base learning rate 0.0001 and the power of $0.9$ is applied for SGD. The weight decay and the momentum are set to 0.0005 and 0.9 respectively. Batch size is $8$ in our experiments. The model is trained for $10$ epochs on NYUD-Large and KITTI, and is trained for $40$ epochs on NYUD-Small in the ablation study. We perform the data augmentation on the training samples by the following methods. For NYUD-V2, the RGB image and the depth map are randomly resized with ratio $[1, 0.92, 0.86, 0.8, 0.75, 0.7, 0.67]$, randomly flipped in the horizon, and finally randomly cropped with the size $384 \times 384$ for NYUD-V2. The similar process is applied for KITTI but resizing with the ratio $[1, 1.1, 1.2, 1.3, 1.4, 1.5]$ and cropping with $384 \times 512$. Note that the depth map should be scaled with the corresponding resizing ratio.

Evaluation Metrics
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We follow previous methods [@laina2016deeper] to evaluate the performance of monocular depth prediction quantitatively based on following metrics: mean absolute relative error (rel), mean $\log_{10}$ error ($ \log_{10}$), root mean 