Scene flow represents the 3D motion of every point in the dynamic environments. Like the optical flow that represents the motion of pixels in 2D images, 3D motion representation of scene flow benefits many applications, such as autonomous driving and service robot. This paper studies the problem of scene flow estimation from two consecutive 3D point clouds. In this paper, a novel hierarchical neural network with double attention is proposed for learning the correlation of point features in adjacent frames and refining scene flow from coarse to fine layer by layer. The proposed network has a new more-for-less hierarchical architecture. The more-for-less means that the number of input points is greater than the number of output points for scene flow estimation, which brings more input information and balances the precision and resource consumption. In this hierarchical architecture, scene flow of different levels is generated and supervised respectively. A novel attentive embedding module is introduced to aggregate the features of adjacent points using a double attention method in a patch-to-patch manner. The proper layers for flow embedding and flow supervision are carefully considered in our network designment. Experiments show that the proposed network outperforms the state-of-the-art performance of 3D scene flow estimation on the FlyingThings3D and KITTI Scene Flow 2015 datasets. We also apply the proposed network to the realistic LiDAR odometry task, which is a key problem in autonomous driving. The experiment results demonstrate that our proposed network can outperform the ICP-based method and shows good practical application ability. The source codes will be released on https://github.com/IRMVLab/HALFlow.

中文翻译：

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3D 点云场景流的分层注意力学习


场景流代表动态环境中每个点的 3D 运动。与表示 2D 图像中像素运动的光流一样，场景流的 3D 运动表示有利于许多应用，例如自动驾驶和服务机器人。本文研究了两个连续 3D 点云的场景流估计问题。本文提出了一种新颖的双重关注分层神经网络，用于学习相邻帧中点特征的相关性，并逐层细化场景流从粗到细。所提出的网络具有新的多为少的分层架构。多换少是指场景流估计的输入点数大于输出点数，带来更多的输入信息，平衡精度和资源消耗。在这种分层架构中，分别生成和监督不同级别的场景流。引入了一种新颖的注意嵌入模块，使用双重注意方法以补丁到补丁的方式聚合相邻点的特征。我们的网络设计中仔细考虑了流嵌入和流监督的适当层。实验表明，所提出的网络在 FlyingThings3D 和 KITTI Scene Flow 2015 数据集上优于 3D 场景流估计的最新性能。我们还将所提出的网络应用于现实的 LiDAR 里程计任务，这是自动驾驶的关键问题。实验结果表明，我们提出的网络可以优于基于ICP的方法，并显示出良好的实际应用能力。源代码将在 https://github.com/IRMVLab/HALFlow 上发布。