通过聚类学习逐像素连续深度表示用于深度补全
Learning Pixel-wise Continuous Depth Representation via Clustering for Depth Completion
AI总结：
本文提出基于聚类的深度补全框架 CluDe，通过迭代更新深度中心并估计偏移量，学习逐像素连续深度表示，有效减少边界深度拖影，在 VOID 和 KITTI 数据集上取得领先性能。
摘要：
深度补全是计算机视觉中一个长期存在的挑战，近年来基于分类的方法取得了巨大进展。然而，大多数现有的基于分类的方法依赖预定义的像素共享且离散的深度值作为深度类别。这种表示无法捕捉符合真实深度分布的连续深度值，导致边界区域出现深度拖影。为了解决这一问题，我们从聚类视角重新审视深度补全，并提出了一种新颖的基于聚类的框架 CluDe，其重点是学习逐像素且连续的深度表示。CluDe 的核心思想是在真实深度分布的驱动下，迭代地将像素共享且离散的深度表示更新为其对应的逐像素且连续的表示。具体来说，CluDe 首先利用深度值聚类来学习一组深度中心作为深度表示。虽然这些深度中心是像素共享且离散的，但与预定义的深度类别相比，它们更符合真实深度分布。然后，CluDe 为这些深度中心估计偏移量，使其能够沿深度分布的深度轴动态调整，从而生成逐像素且连续的深度表示。大量实验表明，CluDe 通过利用逐像素且连续的深度表示，成功减少了物体边界周围的深度拖影。此外，CluDe 在 VOID 数据集上取得了最先进的性能，并在 KITTI 数据集上优于基于分类的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
