\begin{tabular}{c | c | c | c | c | c | c | c}
{\bf Method} & {\bf Setting} & {\bf SILog} & {\bf sqErrorRel} & {\bf absErrorRel} & {\bf iRMSE} & {\bf Runtime} & {\bf Environment}\\ \hline
DeepLab & & 10.80 & 2.19 \% & 8.94 \% & 11.77 & 0.1 s / GPU & \\
MPSD & & 11.12 & 2.07 \% & 8.99 \% & 11.56 & 0.1 s / GPU & \\
GSM & & 11.23 & 2.13 \% & 8.88 \% & 12.65 & 0.06 s / GPU & \\
PWA & & 11.45 & 2.30 \% & 9.05 \% & 12.32 & 0.025 s / GPU & \\
Anonymous & & 11.54 & 2.35 \% & 9.12 \% & 12.38 & 0.08 s / GPU & \\
GSM & & 11.56 & 2.25 \% & 8.99 \% & 12.44 & 0.05 s / GPU & \\
LCI & & 11.59 & 2.21 \% & 9.09 \% & 12.18 & 0.03 s / GPU & \\
BANet & & 11.61 & 2.29 \% & 9.38 \% & 12.23 & 0.04 s / GPU & S. Aich, J. Vianney, M. Islam, M. Kaur and B. Liu: Bidirectional Attention Network for Monocular Depth Estimation. 2020.\\
BTS & & 11.67 & 2.21 \% & 9.04 \% & 12.23 & 0.06 s / GPU & J. Lee, M. Han, D. Ko and I. Suh: From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation. 2019.\\
DL\_61 (DORN) & & 11.77 & 2.23 \% & 8.78 \% & 12.98 & 0.5 s / GPU & H. Fu, M. Gong, C. Wang, K. Batmanghelich and D. Tao: Deep Ordinal Regression Network for Monocular Depth Estimation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018.\\
RefinedMPL & & 11.80 & 2.31 \% & 10.09 \% & 13.39 & 0.05 s / GPU & J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.\\
BiNet-SOC & & 11.83 & 2.66 \% & 10.12 \% & 12.79 & 0.04 s / GPU & \\
BTS-256 & & 12.05 & 2.43 \% & 9.39 \% & 13.11 & 0.1 s / GPU & \\
GAC & & 12.13 & 2.61 \% & 9.41 \% & 12.65 & 0.05 s / GPU & \\
DL\_SORD\_SL & & 12.39 & 2.49 \% & 10.10 \% & 13.48 & 0.8 s / GPU & R. Diaz and A. Marathe: Soft Labels for Ordinal Regression. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.\\
VNL & & 12.65 & 2.46 \% & 10.15 \% & 13.02 & 0.5 s / 1 core & Y. Wei, Y. Liu, C. Shen and Y. Yan: Enforcing geometric constraints of virtual normal for depth prediction. 2019.\\
DS-SIDENet\_ROB & & 12.86 & 2.87 \% & 10.03 \% & 14.40 & 0.35 s / GPU & H. Ren, M. El-Khamy and J. Lee: Deep Robust Single Image Depth Estimation Neural Network Using Scene Understanding. IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPRW) 2019.\\
DL\_SORD\_SQ & & 13.00 & 2.95 \% & 10.38 \% & 13.78 & 0.88 s / GPU & R. Diaz and A. Marathe: Soft Labels for Ordinal Regression. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.\\
PAP & & 13.08 & 2.72 \% & 10.27 \% & 13.95 & 0.18 s / GPU & Z. Zhang, Z. Cui, C. Xu, Y. Yan, N. Sebe and J. Yang: Pattern-Affinitive Propagation Across Depth, Surface Normal and Semantic Segmentation. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.\\
VGG16-UNet & & 13.41 & 2.86 \% & 10.60 \% & 15.06 & 0.16 s / GPU & X. Guo, H. Li, S. Yi, J. Ren and X. Wang: Learning monocular depth by distilling cross-domain stereo networks. Proceedings of the European Conference on Computer Vision (ECCV) 2018.\\
DORN\_ROB & & 13.53 & 3.06 \% & 10.35 \% & 15.96 & 2 s / GPU & H. Fu, M. Gong, C. Wang, K. Batmanghelich and D. Tao: Deep Ordinal Regression Network for Monocular Depth Estimation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018.\\
g2s [WACV 678] & & 14.16 & 3.65 \% & 11.40 \% & 15.53 & 0.04 s / GPU & \\
SSDE & & 14.45 & 3.60 \% & 11.47 \% & 15.52 & 0.1 s / GPU & ERROR: Wrong syntax in BIBTEX file.\\
DABC\_ROB & & 14.49 & 4.08 \% & 12.72 \% & 15.53 & 0.7 s / GPU & R. Li, K. Xian, C. Shen, Z. Cao, H. Lu and L. Hang: Deep attention-based classification network for robust depth prediction. Proceedings of the Asian Conference on Computer Vision (ACCV) 2018.\\
BTSREF\_RVC & & 14.67 & 3.12 \% & 12.42 \% & 16.84 & 0.1 s / 1 core & J. Lee, M. Han, D. Ko and I. Suh: From big to small: Multi-scale local planar guidance for monocular depth estimation. arXiv preprint arXiv:1907.10326 2019.\\
SDNet & & 14.68 & 3.90 \% & 12.31 \% & 15.96 & 0.2 s / GPU & M. Ochs, A. Kretz and R. Mester: SDNet: Semantic Guided Depth Estimation Network. German Conference on Pattern Recognition (GCPR) 2019.\\
APMoE\_base\_ROB & & 14.74 & 3.88 \% & 11.74 \% & 15.63 & 0.2 s / GPU & S. Kong and C. Fowlkes: Pixel-wise Attentional Gating for Parsimonious Pixel Labeling. arxiv 1805.01556 2018.\\
FIS-Nets & & 14.76 & 3.56 \% & 11.41 \% & 15.74 & 0.06 s / 1 core & \\
MonoDeMo & & 14.84 & 4.04 \% & 12.28 \% & 15.69 & 0.01 s / GPU & \\
CSWS\_E\_ROB & & 14.85 & 3.48 \% & 11.84 \% & 16.38 & 0.2 s / 1 core & M. Bo Li: Monocular Depth Estimation with Hierarchical Fusion of Dilated CNNs and Soft-Weighted-Sum Inference. 2018.\\
HBC & & 15.18 & 3.79 \% & 12.33 \% & 17.86 & 0.05 s / GPU & H. Jiang and R. Huang: Hierarchical Binary Classification for Monocular Depth Estimation. IEEE International Conference on Robotics and Biomimetics 2019.\\
SGDepth & & 15.30 & 5.00 \% & 13.29 \% & 15.80 & 0.1 s / GPU & M. Klingner, J. Termöhlen, J. Mikolajczyk and T. Fingscheidt: Self-Supervised Monocular Depth Estimation: Solving the Dynamic Object Problem by Semantic Guidance. ECCV 2020.\\
DHGRL & & 15.47 & 4.04 \% & 12.52 \% & 15.72 & 0.2 s / GPU & Z. Zhang, C. Xu, J. Yang, Y. Tai and L. Chen: Deep hierarchical guidance and regularization learning for end-to-end depth estimation. Pattern Recognition 2018.\\
packnSFMHR\_RVC & & 15.80 & 4.73 \% & 12.28 \% & 17.96 & 0.5 s / GPU & V. Guizilini, R. Ambrus, S. Pillai, A. Raventos and A. Gaidon: 3D Packing for Self-Supervised Monocular Depth Estimation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) .\\
Mono-pad-net & & 15.87 & 4.60 \% & 13.10 \% & 16.98 & 0.1 s / 1 core & \\
MultiDepth & & 16.05 & 3.89 \% & 13.82 \% & 18.21 & 0.01 s / GPU & L. Liebel and M. Körner: MultiDepth: Single-Image Depth Estimation via Multi-Task Regression and Classification. IEEE Intelligent Transportation Systems Conference (ITSC) 2019.\\
LSIM & & 17.92 & 6.88 \% & 14.04 \% & 17.62 & 0.08 s / GPU & M. Goldman, T. Hassner and S. Avidan: Learn Stereo, Infer Mono: Siamese Networks for Self-Supervised, Monocular, Depth Estimation. Computer Vision and Pattern Recognition Workshops (CVPRW) 2019.\\
RMDP\_RVC & & 18.63 & 7.11 \% & 22.11 \% & 37.62 & 0.07 s / 1 core & \\
BESEG & & 23.91 & 24.14 \% & 27.83 \% & 30.52 & 3 s / GPU &
\end{tabular}