# SPDepth: Enhancing Self-Supervised Indoor Monocular Depth Estimation via Self-Propagation
## 5 Conclusions and Discussion  
In this paper, an enhanced self-supervised indoor monocular depth estimation network SPDepth is proposed. To meet the challenge of not sufficient self-supervision information, we introduce a learnable module instead of burdening the loss function. A self-propagation module is placed at the end of SPDepth to propagate high-quality depth results from supervised pixels to unsupervised ones. The computation of self-propagation is based on local window self-attention. The depth results from DepthCNN are partitioned into windows. In terms of the window attention scores, the depths are weighted. The propagation is limited to a local window because local depth similarities are much more significant than global. The contributions are proven effective through experiments conducted on the NYU Depth V2 dataset. Within the generalization research, our SPDepth achieves nearly the same performance with the baseline. It is also observed that SPDepth cannot well distinguish the depth differences between planar regions and their adjacent regions. The precision of depth self-propagation needs further improvement. For future work, the relative position bias will be added into the computation of self-propagation. The bias term can provide relative position information for better handling of the propagation process. The Transformer module will also be exploited to enhance the features for computing self-similarities. Conceptualization, H.Z., X.G. and B.Z. ; methodology, S.S.; software, X.G. ; formal analysis, X.G. and S.S.; investigation, X.G. ; resources, H.Z. and X.L. ; data curation, X.G. ; writing--original draft preparation, X.G. ; writing--review and editing, S.S., N.L. and X.L. ; visualization, X.G. ; supervision, H.Z. and N.L. ; project administration, N.L., X.L. and B.Z. ; funding acquisition, H.Z. and B.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by the Application Innovation Project of CASC (grant number: KZ36006202); National Key Research and Development Program of China (grant number: 20237 FC3300029); Zhejiang Provincial Natural Science Foundation of China (grant number: LD24F020007); \"One Thousand Plan\" projects in Jiangxi Province (grant number: Jssg2023102268). The data were derived from the following resources available in the public domain: [[https://cs.nyu.edu/~fergus/datasets/nyu_depth_v2.html](https://cs.nyu.edu/~fergus/datasets/nyu_depth_v2.html)]([https://cs.nyu.edu/~fergus/datasets/nyu_depth_v2.html](https://cs.nyu.edu/~fergus/datasets/nyu_depth_v2.html)) (accessed on 14 October 2024) and [[https://www.microsoft.com/en-us/research/project/rgb-d-dataset-7-scenes/](https://www.microsoft.com/en-us/research/project/rgb-d-dataset-7-scenes/)]([https://www.microsoft.com/en-us/research/project/rgb-d-dataset-7-scenes/](https://www.microsoft.com/en-us/research/project/rgb-d-dataset-7-scenes/)) (accessed on 14 October 2024). The authors declare no conflicts of interest. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline (_C,H,W_) & **REL \(\downarrow\)** & **RMS \(\downarrow\)** & **Log10 \(\downarrow\)** & **\$ \textless{} 1.25 \(\uparrow\)** & **\$ \textless{} 1.252 \(\uparrow\)** & **\$ \textless{} 1.253 \(\uparrow\)** & **Time \(\downarrow\)** & **Memory \(\downarrow\)** \\ \hline (64, 144, 192) & **0.159** & **0.585** & **0.067** & **0.776** & **0.946** & **0.986** & **15.05 h** & **2.30 G** \\ (64, 72, 96) & 0.161 & 0.593 & 0.068 & 0.771 & 0.943 & **0.986** & 16.07 h & **2.30 G** \\ (128, 36, 48) & 0.162 & 0.596 & 0.068 & 0.768 & 0.944 & 0.985 & 24.22 h & 2.90 G \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation studies of feature \(F_{ori}\). \(C\) is the number of feature channels. \(H\) and \(W\) are the height and width of the feature. The best results are shown in bold. \(\downarrow\) means the lower the value, the better. \(\uparrow\) means the higher the value, the better.