论文标题
学习联合2d-3d表示深度完成
Learning Joint 2D-3D Representations for Depth Completion
论文作者
论文摘要
在本文中，我们解决了从RGBD数据完成深度完成的问题。为了实现这一目标，我们设计了一个简单而有效的神经网络块，该块学会了提取关节2D和3D功能。具体而言，该块由两个特定于域的子网络组成，它们在图像像素上应用2D卷积，并在3D点上连续卷积，其输出功能融合在图像空间中。我们仅通过堆叠提出的块来构建深度完成网络，该块的优势是学习层次结构表示，这些表示在多个级别的2D和3D空间之间完全融合。我们证明了我们的方法对挑战性的Kitti深度完成基准的有效性，并表明我们的方法表现优于最先进的方法。
In this paper, we tackle the problem of depth completion from RGBD data. Towards this goal, we design a simple yet effective neural network block that learns to extract joint 2D and 3D features. Specifically, the block consists of two domain-specific sub-networks that apply 2D convolution on image pixels and continuous convolution on 3D points, with their output features fused in image space. We build the depth completion network simply by stacking the proposed block, which has the advantage of learning hierarchical representations that are fully fused between 2D and 3D spaces at multiple levels. We demonstrate the effectiveness of our approach on the challenging KITTI depth completion benchmark and show that our approach outperforms the state-of-the-art.
