## 4 Experiments
### Comparison to State-of-the-Art Techniques  
We assess our methodology by comparing it against leading, state-of-the-art techniques within the field of guided depth completion. The sparse-to-dense method [6] processes sparse depth measurements as input, setting unobserved areas to an initial value of zero. By leveraging a guidance image, it systematically generates a dense depth map through an end-to-end process. The sparse-to-dense(SS) method [9] refines the approach in [6] by integrating self-supervision through photometric loss derived from consecutive frames. Meanwhile, CSPN [7] further enhances the SS method [6] by employing a recurrent spatial propagation mechanism to refine depth estimation. Nconv-CNN [45] integrates a normalized convolutional layer to effectively process sparse inputs while employing a confidence map for unguided depth completion. The output generated by this unguided network is subsequently fused with the RGB image to enhance the performance of guided depth completion. In contrast, DeepLiDAR [12] employs sparse data as its primary input and incorporates surface normal estimation as an intermediary process to generate dense depth maps. This approach relies on ground-truth surface normals for supervised training. In contrast, \(D^{3}\)-Random is a modified version of deep depth densification (\(D^{3}\)) [15] that generates an intermediate dense depth map from sparse data. It employs nearest neighbor interpolation to enhance the initial sparse depth measurements before passing them into a depth completion network. In its initial form, \(D^{3}\) utilizes a uniform grid with over 500 sampled points as sparse measurements, ensuring a well-balanced distribution and higher density. For an equitable assessment, we retrained and reassessed \(D^{3}\) using the same sparse measurement pattern as our approach, referring to this modified version as \(D^{3}\)-Random. In contrast, the NLSPN [8] enhances depth completion by leveraging non-local spatial information to iteratively refine and enhance the depth map. Table 1 demonstrates that the proposed few-shot learning framework substantially enhances the NLSPN performance [8] across all three evaluation criteria using only one-tenth of the labeled data required for the same task. Table 2 demonstrates our method's performance on the KITTI benchmark. By leveraging both labeled and pseudo-labeled data through our self-training framework, we achieve competitive results compared to existing supervised approaches while using only 10% of the labeled training samples. The integration of pixel-wise knowledge distillation effectively preserves structural details in challenging outdoor scenarios with dynamic objects and large depth ranges. The student model significantly outperforms the teacher model by leveraging pseudo-labels strategically. Although the student model emulates the teacher model's behavior, noise is incorporated into the student model while being excluded from the teacher model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **RMSE** & **REL** & \(\delta_{\textbf{125}}\) & \(\delta_{\textbf{125}^{2}}\) & \(\delta_{\textbf{125}^{3}}\) \\ \hline Sparse-to-Dense [6] & 0.230 & 0.044 & 97.1 & 99.4 & 99.8 \\ Sparse-to-Dense (SS) [9] & 0.132 & 0.027 & 99.0 & 99.9 & 100.0 \\ CSPN [7] & 0.117 & 0.016 & 99.2 & 99.9 & 100.0 \\ Nonv-CNN [45] & 0.125 & 0.017 & 99.1 & 99.8 & 100.0 \\ DeepLiDAR [12] & 0.115 & 0.022 & 99.3 & 99.9 & 100.0 \\ D\({}^{3}\)-Random [15] & 0.157 & 0.025 & 99.0 & 99.8 & 100.0 \\ NLSPN [8] & 0.092 & 0.012 & 99.6 & 99.9 & 100.0 \\ Ours & 0.090 & 0.011 & 99.7 & 99.9 & 100.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison with state-of-the-art methods based on the NYUv2 dataset.