GraphCSPN:基于动态GCNs的几何感知深度补全

Xin Liu, Xiaofei Shao, Boqian Wang, Yali Li, Shengjin Wang
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引用次数: 7

摘要

图像引导深度补全旨在借助对齐的彩色图像从稀疏深度测量中恢复每像素密集深度地图,从机器人到自动驾驶都有广泛的应用。然而,以前的方法尚未充分探索稀疏到密集深度完井的三维性质。在这项工作中,我们提出了一种基于图卷积的空间传播网络(GraphCSPN)作为深度补全的通用方法。首先,与以前的方法不同,我们利用卷积神经网络和图神经网络以互补的方式进行几何表示学习。此外,所提出的网络明确地结合了可学习的几何约束,以正则化在三维空间而不是二维平面上进行的传播过程。此外,我们利用特征补丁序列构建图,并在传播过程中使用边缘关注模块动态更新图,从而更好地捕获局部相邻特征和远距离全局关系。在室内NYU-Depth-v2和室外KITTI数据集上进行的大量实验表明,我们的方法达到了最先进的性能,特别是在仅使用几个传播步骤的情况下进行比较时。代码和模型可在项目页面中获得。
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GraphCSPN: Geometry-Aware Depth Completion via Dynamic GCNs
Image guided depth completion aims to recover per-pixel dense depth maps from sparse depth measurements with the help of aligned color images, which has a wide range of applications from robotics to autonomous driving. However, the 3D nature of sparse-to-dense depth completion has not been fully explored by previous methods. In this work, we propose a Graph Convolution based Spatial Propagation Network (GraphCSPN) as a general approach for depth completion. First, unlike previous methods, we leverage convolution neural networks as well as graph neural networks in a complementary way for geometric representation learning. In addition, the proposed networks explicitly incorporate learnable geometric constraints to regularize the propagation process performed in three-dimensional space rather than in two-dimensional plane. Furthermore, we construct the graph utilizing sequences of feature patches, and update it dynamically with an edge attention module during propagation, so as to better capture both the local neighboring features and global relationships over long distance. Extensive experiments on both indoor NYU-Depth-v2 and outdoor KITTI datasets demonstrate that our method achieves the state-of-the-art performance, especially when compared in the case of using only a few propagation steps. Code and models are available at the project page.
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