论文标题
使用学习的基础有效地完成深度
Efficient Depth Completion Using Learned Bases
论文作者
论文摘要
在本文中，我们提出了一个新的全球几何限制，以完成深度完成。通过假设深度图通常位于低维子空间上，可以通过全分辨率主深度底座的加权总和来近似密集的深度图。深度场的主要组成部分可以从自然深度图中学到。给定的稀疏深度点是限制加权过程的数据术语。当输入深度太稀疏时，恢复的致密深度图通常会过度平滑。为了解决此问题，我们添加了一个颜色指导的自动回归模型作为另一个正则化术语。它假设重建的深度图应在随附的颜色图像中具有相同的非局部相似性。我们的颜色引导的PCA深度完成方法具有封闭形式的溶液，因此可以有效地解决，并且比仅PCA的方法更准确。关于Kitti和Middlebury数据集的广泛实验证明了我们提出的方法的出色性能。
In this paper, we propose a new global geometry constraint for depth completion. By assuming depth maps often lay on low dimensional subspaces, a dense depth map can be approximated by a weighted sum of full-resolution principal depth bases. The principal components of depth fields can be learned from natural depth maps. The given sparse depth points are served as a data term to constrain the weighting process. When the input depth points are too sparse, the recovered dense depth maps are often over smoothed. To address this issue, we add a colour-guided auto-regression model as another regularization term. It assumes the reconstructed depth maps should share the same nonlocal similarity in the accompanying colour image. Our colour-guided PCA depth completion method has closed-form solutions, thus can be efficiently solved and is significantly more accurate than PCA only method. Extensive experiments on KITTI and Middlebury datasets demonstrate the superior performance of our proposed method.
