Learning Pixel-wise Continuous Depth Representation via Clustering for
  Depth Completion
Chen Shenglun Zhang Hong Ma XinZhu Wang Zhihui Li Haojie
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.