## 1 Introduction  
Precise dense depth maps are fundamental to various computer vision applications, including facilitating autonomous navigation [1], augmented reality interactions [2], 3D modeling techniques [3], and supporting real-time localization and mapping systems [4]. Despite advancements in depth-sensing technology, hardware devices often hinder depth sensors from generating dense depth maps for research purposes. To address this limitation, the depth completion technique [5] converts sparse depth inputs into rich, dense depth maps, enabling the derivation of enhanced depth information and corresponding color visuals from limited sparse datasets. Recent advancements in deep neural networks (DNNs) have substantially enhanced depth completion tasks. The sparse-to-dense method [6] leverages a single deep regression network to handle raw RGB-D data, addressing the challenge of producing dense depth maps from sparsely sampled raw RGB images. Meanwhile, the convolutional spatial propagation network (CSPN) [7] employs an efficient linear propagation model to effectively capture relationships among pixel neighborhoods. This mechanism refines depth outputs obtained from state-of-the-art methods while converting sparse depth samples into dense maps through an integrated propagation mechanism. Building upon this foundation, the non-local spatial propagation network (NLSPN) [8] further advances this approach by introducing a learnable affinity normalization mechanism. This method estimates non-local neighboring pixels and their respective affinities, integrating them with an initial depth map and pixel confidence values to enhance affinity learning and resilience against inconsistencies at depth boundaries. However, most existing DNN-based depth completion techniques rely heavily on extensive supervised data, yet gathering and disposing of large numbers of sparse depth maps are both challenging and expensive [5]. To address this limitation, this study employs a few-shot learning framework to predict the dense depth for the image. Figure 1d,e illustrate the testing results obtained using the advanced NLSPN [8] architecture. When trained on a randomly selected 10% of the NYUv2 dataset, the model produces outputs that exhibit more noise and blurriness compared to those trained on the full NYUv2 dataset. Therefore, an inadequate dataset jeopardizes the model's generalization ability. Exploring the impact of a limited training dataset on depth completion is crucial. DNN-based depth completion methods typically depend on extensive supervised data samples. However, acquiring and processing large-scale, sparse depth maps are both challenging and costly. Moreover, current real-world datasets lack pixel-accurate ground truth due to depth sensor limitations. Furthermore, semi-dense annotations are often imprecise and affected by occlusions, dynamic objects, and other distortions. To address data scarcity, several researchers are exploring self-supervised frameworks [9; 10] to compensate for limited ground-truth depth information. These methods derive supervisory signals from the dataset itself, eliminating the need for extensive labeled datasets. By leveraging intrinsic data patterns and correlations, self-supervised learning  
Figure 1: Results from various depth estimation methods demonstrating the generation of a depth map guided by a reference RGB image and sparse depth inputs. (**a**) Reference RGB image. (**b**) Sparse depth dataset. (**c**) Ground-truth depth map. Depth prediction by NLSPN trained on (**d**) a 10% subset of the NYUv2 dataset and (**e**) the full NYUv2 dataset. (**f**) Depth prediction from the proposed model assessed on a 10% subset of the NYUv2 dataset. enables depth estimation without requiring manual annotations. This strategy mitigates the difficulty of obtaining extensive labeled depth datasets while enhancing the model's capacity to deduce depth from partial or corrupted inputs. Knowledge distillation (KD) is crucial in self-supervised learning by boosting performance, stability, and efficiency.