Depth completion is a long-standing challenge in computer vision, where classification-based methods have made tremendous progress in recent years. However, most existing classification-based methods rely on pre-defined pixel-shared and discrete depth values as depth categories. This representation fails to capture the continuous depth values that conform to the real depth distribution, leading to depth smearing in boundary regions. To address this issue, we revisit depth completion from the clustering perspective and propose a novel clustering-based framework called CluDe which focuses on learning the pixel-wise and continuous depth representation. The key idea of CluDe is to iteratively update the pixel-shared and discrete depth representation to its corresponding pixel-wise and continuous counterpart, driven by the real depth distribution. Specifically, CluDe first utilizes depth value clustering to learn a set of depth centers as the depth representation. While these depth centers are pixel-shared and discrete, they are more in line with the real depth distribution compared to pre-defined depth categories. Then, CluDe estimates offsets for these depth centers, enabling their dynamic adjustment along the depth axis of the depth distribution to generate the pixel-wise and continuous depth representation. Extensive experiments demonstrate that CluDe successfully reduces depth smearing around object boundaries by utilizing pixel-wise and continuous depth representation. Furthermore, CluDe achieves state-of-the-art performance on the VOID datasets and outperforms classification-based methods on the KITTI dataset.

深度完整性的一个长期挑战是计算机视觉领域中的一个难题。近年来，基于分类的方法取得了巨大的进展。然而，大多数现有的基于分类的方法依赖于预定义的像素共享和离散深度值作为深度类别。本文从聚类的角度重新思考深度完整性，并提出了一种名为CluDe的新型基于聚类的框架，它专注于学习像素级和连续深度表示。CluDe的关键思想是利用像素值的聚类来学习一组深度中心作为深度表示。然后，CluDe对这些深度中心估计偏移量，使其可以在深度分布的深度轴上进行动态调整，从而生成像素级和连续深度表示。广泛的实验表明，CluDe成功地利用像素级和连续深度表示降低了对象边界处的深度模糊。此外，CluDe在VOID数据集上实现了最先进的性能，并在KITTI数据集上超过了基于分类的方法。

通过聚类学习像素级连续深度表示以用于深度完整