论文标题
非本地空间传播网络，以完成深度完成
Non-Local Spatial Propagation Network for Depth Completion
论文作者
论文摘要
在本文中，我们提出了一个可靠，有效的端到端非本地空间传播网络，以实现深度完成。所提出的网络将RGB和稀疏深度图像作为输入和估计每个像素的非本地邻居及其亲和力，以及具有像素智能感知的初始深度图。然后，基于预测的非本地邻居和相应的亲和力，通过其置信度和非本地空间传播程序进行迭代深度预测。与以前利用固定本地邻居的算法不同，所提出的算法有效地避免了当地邻居无关，并集中于传播过程中相关的非本地邻居。此外，我们引入了可学习的亲和力归一化，以更好地学习与常规方法相比的亲和力组合。所提出的算法本质上对深度边界上的混合深度问题具有强大的核心，这是现有深度估计/完成算法的主要问题之一。室内和室外数据集的实验结果表明，就深度完成精度和鲁棒性而言，所提出的算法优于常规算法。我们的实施在项目页面上公开可用。
In this paper, we propose a robust and efficient end-to-end non-local spatial propagation network for depth completion. The proposed network takes RGB and sparse depth images as inputs and estimates non-local neighbors and their affinities of each pixel, as well as an initial depth map with pixel-wise confidences. The initial depth prediction is then iteratively refined by its confidence and non-local spatial propagation procedure based on the predicted non-local neighbors and corresponding affinities. Unlike previous algorithms that utilize fixed-local neighbors, the proposed algorithm effectively avoids irrelevant local neighbors and concentrates on relevant non-local neighbors during propagation. In addition, we introduce a learnable affinity normalization to better learn the affinity combinations compared to conventional methods. The proposed algorithm is inherently robust to the mixed-depth problem on depth boundaries, which is one of the major issues for existing depth estimation/completion algorithms. Experimental results on indoor and outdoor datasets demonstrate that the proposed algorithm is superior to conventional algorithms in terms of depth completion accuracy and robustness to the mixed-depth problem. Our implementation is publicly available on the project page.
